Build a classifier to predict the Pass/Fail yield of a particular process entity and analyse whether all the features are required to build the model or not.
#import vaex
import numpy as np
import pandas as pd
from scipy.stats import zscore
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import scipy.stats as stats
#from imblearn.over_sampling import SMOTE
from sklearn.metrics import average_precision_score, confusion_matrix, accuracy_score, classification_report, plot_confusion_matrix
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.metrics import recall_score
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import LeaveOneOut
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.decomposition import PCA
from sklearn.linear_model import RidgeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
print('''\n\033[1m''' + '''Loading Dataset....''' + '''\033[0m''')
pd.set_option("display.max_columns", None)
sd = pd.read_csv("signal-data.csv")
Loading Dataset....
print('''\n\033[1m''' + '''Getting 10 rows of data''' + '''\033[0m''')
sd.head(5)
Getting 10 rows of data
| Time | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 358 | 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 449 | 450 | 451 | 452 | 453 | 454 | 455 | 456 | 457 | 458 | 459 | 460 | 461 | 462 | 463 | 464 | 465 | 466 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 481 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 492 | 493 | 494 | 495 | 496 | 497 | 498 | 499 | 500 | 501 | 502 | 503 | 504 | 505 | 506 | 507 | 508 | 509 | 510 | 511 | 512 | 513 | 514 | 515 | 516 | 517 | 518 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 528 | 529 | 530 | 531 | 532 | 533 | 534 | 535 | 536 | 537 | 538 | 539 | 540 | 541 | 542 | 543 | 544 | 545 | 546 | 547 | 548 | 549 | 550 | 551 | 552 | 553 | 554 | 555 | 556 | 557 | 558 | 559 | 560 | 561 | 562 | 563 | 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | 578 | 579 | 580 | 581 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2008-07-19 11:55:00 | 3030.93 | 2564.00 | 2187.7333 | 1411.1265 | 1.3602 | 100.0 | 97.6133 | 0.1242 | 1.5005 | 0.0162 | -0.0034 | 0.9455 | 202.4396 | 0.0 | 7.9558 | 414.8710 | 10.0433 | 0.9680 | 192.3963 | 12.5190 | 1.4026 | -5419.00 | 2916.50 | -4043.75 | 751.00 | 0.8955 | 1.7730 | 3.0490 | 64.2333 | 2.0222 | 0.1632 | 3.5191 | 83.3971 | 9.5126 | 50.6170 | 64.2588 | 49.3830 | 66.3141 | 86.9555 | 117.5132 | 61.29 | 4.515 | 70.0 | 352.7173 | 10.1841 | 130.3691 | 723.3092 | 1.3072 | 141.2282 | 1.0 | 624.3145 | 218.3174 | 0.0 | 4.592 | 4.841 | 2834.0 | 0.9317 | 0.9484 | 4.7057 | -1.7264 | 350.9264 | 10.6231 | 108.6427 | 16.1445 | 21.7264 | 29.5367 | 693.7724 | 0.9226 | 148.6009 | 1.0 | 608.1700 | 84.0793 | NaN | NaN | 0.0 | 0.0126 | -0.0206 | 0.0141 | -0.0307 | -0.0083 | -0.0026 | -0.0567 | -0.0044 | 7.2163 | 0.1320 | NaN | 2.3895 | 0.9690 | 1747.6049 | 0.1841 | 8671.9301 | -0.3274 | -0.0055 | -0.0001 | 0.0001 | 0.0003 | -0.2786 | 0.0 | 0.3974 | -0.0251 | 0.0002 | 0.0002 | 0.1350 | -0.0042 | 0.0003 | 0.0056 | 0.0000 | -0.2468 | 0.3196 | NaN | NaN | NaN | NaN | 0.9460 | 0.0 | 748.6115 | 0.9908 | 58.4306 | 0.6002 | 0.9804 | 6.3788 | 15.88 | 2.639 | 15.94 | 15.93 | 0.8656 | 3.353 | 0.4098 | 3.188 | -0.0473 | 0.7243 | 0.9960 | 2.2967 | 1000.7263 | 39.2373 | 123.0 | 111.3 | 75.2 | 46.2000 | 350.6710 | 0.3948 | 0.0 | 6.78 | 0.0034 | 0.0898 | 0.0850 | 0.0358 | 0.0328 | 12.2566 | 0.0 | 4.271 | 10.284 | 0.4734 | 0.0167 | 11.8901 | 0.41 | 0.0506 | NaN | NaN | 1017.0 | 967.0 | 1066.0 | 368.0 | 0.090 | 0.048 | 0.095 | 2.0 | 0.9 | 0.069 | 0.046 | 0.7250 | 0.1139 | 0.3183 | 0.5888 | 0.3184 | 0.9499 | 0.3979 | 0.160 | 0.0 | 0.0 | 20.95 | 0.333 | 12.49 | 16.713 | 0.0803 | 5.72 | 0.0 | 11.19 | 65.363 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.292 | 5.38 | 20.10 | 0.296 | 10.62 | 10.30 | 5.38 | 4.040 | 16.230 | 0.2951 | 8.64 | 0.0 | 10.30 | 97.314 | 0.0 | 0.0772 | 0.0599 | 0.0700 | 0.0547 | 0.0704 | 0.0520 | 0.0301 | 0.1135 | 3.4789 | 0.0010 | NaN | 0.0707 | 0.0211 | 175.2173 | 0.0315 | 1940.3994 | 0.0 | 0.0744 | 0.0546 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0027 | 0.0040 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | 0.0188 | 0.0 | 219.9453 | 0.0011 | 2.8374 | 0.0189 | 0.0050 | 0.4269 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0472 | 40.855 | 4.5152 | 30.9815 | 33.9606 | 22.9057 | 15.9525 | 110.2144 | 0.1310 | 0.0 | 2.5883 | 0.0010 | 0.0319 | 0.0197 | 0.0120 | 0.0109 | 3.9321 | 0.0 | 1.5123 | 3.5811 | 0.1337 | 0.0055 | 3.8447 | 0.1077 | 0.0167 | NaN | NaN | 418.1363 | 398.3185 | 496.1582 | 158.3330 | 0.0373 | 0.0202 | 0.0462 | 0.6083 | 0.3032 | 0.0200 | 0.0174 | 0.2827 | 0.0434 | 0.1342 | 0.2419 | 0.1343 | 0.3670 | 0.1431 | 0.0610 | 0.0 | 0.0 | 0.0 | 6.2698 | 0.1181 | 3.8208 | 5.3737 | 0.0254 | 1.6252 | 0.0 | 3.2461 | 18.0118 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0752 | 1.5989 | 6.5893 | 0.0913 | 3.0911 | 8.4654 | 1.5989 | 1.2293 | 5.3406 | 0.0867 | 2.8551 | 0.0 | 2.9971 | 31.8843 | NaN | NaN | 0.0 | 0.0215 | 0.0274 | 0.0315 | 0.0238 | 0.0206 | 0.0238 | 0.0144 | 0.0491 | 1.2708 | 0.0004 | NaN | 0.0229 | 0.0065 | 55.2039 | 0.0105 | 560.2658 | 0.0 | 0.0170 | 0.0148 | 0.0124 | 0.0114 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0010 | 0.0013 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | 0.0055 | 0.0 | 61.5932 | 0.0003 | 0.9967 | 0.0082 | 0.0017 | 0.1437 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0151 | 14.2396 | 1.4392 | 5.6188 | 3.6721 | 2.9329 | 2.1118 | 24.8504 | 29.0271 | 0.0 | 6.9458 | 2.7380 | 5.9846 | 525.0965 | 0.0000 | 3.4641 | 6.0544 | 0.0 | 53.6840 | 2.4788 | 4.7141 | 1.7275 | 6.1800 | 3.2750 | 3.6084 | 18.7673 | 33.1562 | 26.3617 | 49.0013 | 10.0503 | 2.7073 | 3.1158 | 3.1136 | 44.5055 | 42.2737 | 1.3071 | 0.8693 | 1.1975 | 0.6288 | 0.9163 | 0.6448 | 1.4324 | 0.4576 | 0.1362 | 0.0 | 0.0 | 0.0 | 5.9396 | 3.2698 | 9.5805 | 2.3106 | 6.1463 | 4.0502 | 0.0 | 1.7924 | 29.9394 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.2052 | 311.6377 | 5.7277 | 2.7864 | 9.7752 | 63.7987 | 24.7625 | 13.6778 | 2.3394 | 31.9893 | 5.8142 | 0.0 | 1.6936 | 115.7408 | 0.0 | 613.3069 | 291.4842 | 494.6996 | 178.1759 | 843.1138 | 0.0000 | 53.1098 | 0.0000 | 48.2091 | 0.7578 | NaN | 2.9570 | 2.1739 | 10.0261 | 17.1202 | 22.3756 | 0.0 | 0.0000 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 64.6707 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | 1.9864 | 0.0 | 29.3804 | 0.1094 | 4.8560 | 3.1406 | 0.5064 | 6.6926 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0570 | 4.0825 | 11.5074 | 0.1096 | 0.0078 | 0.0026 | 7.116 | 1.0616 | 395.570 | 75.752 | 0.4234 | 12.93 | 0.78 | 0.1827 | 5.7349 | 0.3363 | 39.8842 | 3.2687 | 1.0297 | 1.0344 | 0.4385 | 0.1039 | 42.3877 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 533.8500 | 2.1113 | 8.95 | 0.3157 | 3.0624 | 0.1026 | 1.6765 | 14.9509 | NaN | NaN | NaN | NaN | 0.5005 | 0.0118 | 0.0035 | 2.3630 | NaN | NaN | NaN | NaN | -1 |
| 1 | 2008-07-19 12:32:00 | 3095.78 | 2465.14 | 2230.4222 | 1463.6606 | 0.8294 | 100.0 | 102.3433 | 0.1247 | 1.4966 | -0.0005 | -0.0148 | 0.9627 | 200.5470 | 0.0 | 10.1548 | 414.7347 | 9.2599 | 0.9701 | 191.2872 | 12.4608 | 1.3825 | -5441.50 | 2604.25 | -3498.75 | -1640.25 | 1.2973 | 2.0143 | 7.3900 | 68.4222 | 2.2667 | 0.2102 | 3.4171 | 84.9052 | 9.7997 | 50.6596 | 64.2828 | 49.3404 | 64.9193 | 87.5241 | 118.1188 | 78.25 | 2.773 | 70.0 | 352.2445 | 10.0373 | 133.1727 | 724.8264 | 1.2887 | 145.8445 | 1.0 | 631.2618 | 205.1695 | 0.0 | 4.590 | 4.842 | 2853.0 | 0.9324 | 0.9479 | 4.6820 | 0.8073 | 352.0073 | 10.3092 | 113.9800 | 10.9036 | 19.1927 | 27.6301 | 697.1964 | 1.1598 | 154.3709 | 1.0 | 620.3582 | 82.3494 | NaN | NaN | 0.0 | -0.0039 | -0.0198 | 0.0004 | -0.0440 | -0.0358 | -0.0120 | -0.0377 | 0.0017 | 6.8043 | 0.1358 | NaN | 2.3754 | 0.9894 | 1931.6464 | 0.1874 | 8407.0299 | 0.1455 | -0.0015 | 0.0000 | -0.0005 | 0.0001 | 0.5854 | 0.0 | -0.9353 | -0.0158 | -0.0004 | -0.0004 | -0.0752 | -0.0045 | 0.0002 | 0.0015 | 0.0000 | 0.0772 | -0.0903 | NaN | NaN | NaN | NaN | 0.9425 | 0.0 | 731.2517 | 0.9902 | 58.6680 | 0.5958 | 0.9731 | 6.5061 | 15.88 | 2.541 | 15.91 | 15.88 | 0.8703 | 2.771 | 0.4138 | 3.272 | -0.0946 | 0.8122 | 0.9985 | 2.2932 | 998.1081 | 37.9213 | 98.0 | 80.3 | 81.0 | 56.2000 | 219.7679 | 0.2301 | 0.0 | 5.70 | 0.0049 | 0.1356 | 0.0600 | 0.0547 | 0.0204 | 12.3319 | 0.0 | 6.285 | 13.077 | 0.5666 | 0.0144 | 11.8428 | 0.35 | 0.0437 | NaN | NaN | 568.0 | 59.0 | 297.0 | 3277.0 | 0.112 | 0.115 | 0.124 | 2.2 | 1.1 | 0.079 | 0.561 | 1.0498 | 0.1917 | 0.4115 | 0.6582 | 0.4115 | 1.0181 | 0.2315 | 0.325 | 0.0 | 0.0 | 17.99 | 0.439 | 10.14 | 16.358 | 0.0892 | 6.92 | 0.0 | 9.05 | 82.986 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.222 | 3.74 | 19.59 | 0.316 | 11.65 | 8.02 | 3.74 | 3.659 | 15.078 | 0.3580 | 8.96 | 0.0 | 8.02 | 134.250 | 0.0 | 0.0566 | 0.0488 | 0.1651 | 0.1578 | 0.0468 | 0.0987 | 0.0734 | 0.0747 | 3.9578 | 0.0050 | NaN | 0.0761 | 0.0014 | 128.4285 | 0.0238 | 1988.0000 | 0.0 | 0.0203 | 0.0236 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0064 | 0.0036 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | 0.0154 | 0.0 | 193.0287 | 0.0007 | 3.8999 | 0.0187 | 0.0086 | 0.5749 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0411 | 29.743 | 3.6327 | 29.0598 | 28.9862 | 22.3163 | 17.4008 | 83.5542 | 0.0767 | 0.0 | 1.8459 | 0.0012 | 0.0440 | 0.0171 | 0.0154 | 0.0069 | 3.9011 | 0.0 | 2.1016 | 3.9483 | 0.1662 | 0.0049 | 3.7836 | 0.1000 | 0.0139 | NaN | NaN | 233.9865 | 26.5879 | 139.2082 | 1529.7622 | 0.0502 | 0.0561 | 0.0591 | 0.8151 | 0.3464 | 0.0291 | 0.1822 | 0.3814 | 0.0715 | 0.1667 | 0.2630 | 0.1667 | 0.3752 | 0.0856 | 0.1214 | 0.0 | 0.0 | 0.0 | 5.6522 | 0.1417 | 2.9939 | 5.2445 | 0.0264 | 1.8045 | 0.0 | 2.7661 | 23.6230 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0778 | 1.1506 | 5.9247 | 0.0878 | 3.3604 | 7.7421 | 1.1506 | 1.1265 | 5.0108 | 0.1013 | 2.4278 | 0.0 | 2.4890 | 41.7080 | NaN | NaN | 0.0 | 0.0142 | 0.0230 | 0.0768 | 0.0729 | 0.0143 | 0.0513 | 0.0399 | 0.0365 | 1.2474 | 0.0017 | NaN | 0.0248 | 0.0005 | 46.3453 | 0.0069 | 677.1873 | 0.0 | 0.0053 | 0.0059 | 0.0081 | 0.0033 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0022 | 0.0013 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | 0.0049 | 0.0 | 65.0999 | 0.0002 | 1.1655 | 0.0068 | 0.0027 | 0.1921 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0120 | 10.5837 | 1.0323 | 4.3465 | 2.5939 | 3.2858 | 2.5197 | 15.0150 | 27.7464 | 0.0 | 5.5695 | 3.9300 | 9.0604 | 0.0000 | 368.9713 | 2.1196 | 6.1491 | 0.0 | 61.8918 | 3.1531 | 6.1188 | 1.4857 | 6.1911 | 2.8088 | 3.1595 | 10.4383 | 2.2655 | 8.4887 | 199.7866 | 8.6336 | 5.7093 | 1.6779 | 3.2153 | 48.5294 | 37.5793 | 16.4174 | 1.2364 | 1.9562 | 0.8123 | 1.0239 | 0.8340 | 1.5683 | 0.2645 | 0.2751 | 0.0 | 0.0 | 0.0 | 5.1072 | 4.3737 | 7.6142 | 2.2568 | 6.9233 | 4.7448 | 0.0 | 1.4336 | 40.4475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.7415 | 463.2883 | 5.5652 | 3.0652 | 10.2211 | 73.5536 | 19.4865 | 13.2430 | 2.1627 | 30.8643 | 5.8042 | 0.0 | 1.2928 | 163.0249 | 0.0 | 0.0000 | 246.7762 | 0.0000 | 359.0444 | 130.6350 | 820.7900 | 194.4371 | 0.0000 | 58.1666 | 3.6822 | NaN | 3.2029 | 0.1441 | 6.6487 | 12.6788 | 23.6469 | 0.0 | 0.0000 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 141.4365 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | 1.6292 | 0.0 | 26.3970 | 0.0673 | 6.6475 | 3.1310 | 0.8832 | 8.8370 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.7910 | 2.9799 | 9.5796 | 0.1096 | 0.0078 | 0.0026 | 7.116 | 1.3526 | 408.798 | 74.640 | 0.7193 | 16.00 | 1.33 | 0.2829 | 7.1196 | 0.4989 | 53.1836 | 3.9139 | 1.7819 | 0.9634 | 0.1745 | 0.0375 | 18.1087 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 535.0164 | 2.4335 | 5.92 | 0.2653 | 2.0111 | 0.0772 | 1.1065 | 10.9003 | 0.0096 | 0.0201 | 0.0060 | 208.2045 | 0.5019 | 0.0223 | 0.0055 | 4.4447 | 0.0096 | 0.0201 | 0.0060 | 208.2045 | -1 |
| 2 | 2008-07-19 13:17:00 | 2932.61 | 2559.94 | 2186.4111 | 1698.0172 | 1.5102 | 100.0 | 95.4878 | 0.1241 | 1.4436 | 0.0041 | 0.0013 | 0.9615 | 202.0179 | 0.0 | 9.5157 | 416.7075 | 9.3144 | 0.9674 | 192.7035 | 12.5404 | 1.4123 | -5447.75 | 2701.75 | -4047.00 | -1916.50 | 1.3122 | 2.0295 | 7.5788 | 67.1333 | 2.3333 | 0.1734 | 3.5986 | 84.7569 | 8.6590 | 50.1530 | 64.1114 | 49.8470 | 65.8389 | 84.7327 | 118.6128 | 14.37 | 5.434 | 70.0 | 364.3782 | 9.8783 | 131.8027 | 734.7924 | 1.2992 | 141.0845 | 1.0 | 637.2655 | 185.7574 | 0.0 | 4.486 | 4.748 | 2936.0 | 0.9139 | 0.9447 | 4.5873 | 23.8245 | 364.5364 | 10.1685 | 115.6273 | 11.3019 | 16.1755 | 24.2829 | 710.5095 | 0.8694 | 145.8000 | 1.0 | 625.9636 | 84.7681 | 140.6972 | 485.2665 | 0.0 | -0.0078 | -0.0326 | -0.0052 | 0.0213 | -0.0054 | -0.1134 | -0.0182 | 0.0287 | 7.1041 | 0.1362 | NaN | 2.4532 | 0.9880 | 1685.8514 | 0.1497 | 9317.1698 | 0.0553 | 0.0006 | -0.0013 | 0.0000 | 0.0002 | -0.1343 | 0.0 | -0.1427 | 0.1218 | 0.0006 | -0.0001 | 0.0134 | -0.0026 | -0.0016 | -0.0006 | 0.0013 | -0.0301 | -0.0728 | NaN | NaN | NaN | 0.4684 | 0.9231 | 0.0 | 718.5777 | 0.9899 | 58.4808 | 0.6015 | 0.9772 | 6.4527 | 15.90 | 2.882 | 15.94 | 15.95 | 0.8798 | 3.094 | 0.4777 | 3.272 | -0.1892 | 0.8194 | 0.9978 | 2.2592 | 998.4440 | 42.0579 | 89.0 | 126.4 | 96.5 | 45.1001 | 306.0380 | 0.3263 | 0.0 | 8.33 | 0.0038 | 0.0754 | 0.0483 | 0.0619 | 0.0221 | 8.2660 | 0.0 | 4.819 | 8.443 | 0.4909 | 0.0177 | 8.2054 | 0.47 | 0.0497 | NaN | NaN | 562.0 | 788.0 | 759.0 | 2100.0 | 0.187 | 0.117 | 0.068 | 2.1 | 1.4 | 0.123 | 0.319 | 1.0824 | 0.0369 | 0.3141 | 0.5753 | 0.3141 | 0.9677 | 0.2706 | 0.326 | 0.0 | 0.0 | 17.78 | 0.745 | 13.31 | 22.912 | 0.1959 | 9.21 | 0.0 | 17.87 | 60.110 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.139 | 5.09 | 19.75 | 0.949 | 9.71 | 16.73 | 5.09 | 11.059 | 22.624 | 0.1164 | 13.30 | 0.0 | 16.73 | 79.618 | 0.0 | 0.0339 | 0.0494 | 0.0696 | 0.0406 | 0.0401 | 0.0840 | 0.0349 | 0.0718 | 2.4266 | 0.0014 | NaN | 0.0963 | 0.0152 | 182.4956 | 0.0284 | 839.6006 | 0.0 | 0.0192 | 0.0170 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0062 | 0.0040 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | 0.1729 | 0.0273 | 0.0 | 104.4042 | 0.0007 | 4.1446 | 0.0733 | 0.0063 | 0.4166 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0487 | 29.621 | 3.9133 | 23.5510 | 41.3837 | 32.6256 | 15.7716 | 97.3868 | 0.1117 | 0.0 | 2.5274 | 0.0012 | 0.0249 | 0.0152 | 0.0157 | 0.0075 | 2.8705 | 0.0 | 1.5306 | 2.5493 | 0.1479 | 0.0059 | 2.8046 | 0.1185 | 0.0167 | NaN | NaN | 251.4536 | 329.6406 | 325.0672 | 902.4576 | 0.0800 | 0.0583 | 0.0326 | 0.6964 | 0.4031 | 0.0416 | 0.1041 | 0.3846 | 0.0151 | 0.1288 | 0.2268 | 0.1288 | 0.3677 | 0.1175 | 0.1261 | 0.0 | 0.0 | 0.0 | 5.7247 | 0.2682 | 3.8541 | 6.1797 | 0.0546 | 2.5680 | 0.0 | 4.6067 | 16.0104 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0243 | 1.5481 | 5.9453 | 0.2777 | 3.1600 | 8.9855 | 1.5481 | 2.9844 | 6.2277 | 0.0353 | 3.7663 | 0.0 | 5.6983 | 24.7959 | 13.5664 | 15.4488 | 0.0 | 0.0105 | 0.0208 | 0.0327 | 0.0171 | 0.0116 | 0.0428 | 0.0154 | 0.0383 | 0.7786 | 0.0005 | NaN | 0.0302 | 0.0046 | 58.0575 | 0.0092 | 283.6616 | 0.0 | 0.0054 | 0.0043 | 0.0030 | 0.0037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0021 | 0.0015 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | 0.0221 | 0.0100 | 0.0 | 28.7334 | 0.0003 | 1.2356 | 0.0190 | 0.0020 | 0.1375 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0190 | 11.4871 | 1.1798 | 4.0782 | 4.3102 | 3.7696 | 2.0627 | 18.0233 | 21.6062 | 0.0 | 8.7236 | 3.0609 | 5.2231 | 0.0000 | 0.0000 | 2.2943 | 4.0917 | 0.0 | 50.6425 | 2.0261 | 5.2707 | 1.8268 | 4.2581 | 3.7479 | 3.5220 | 10.3162 | 29.1663 | 18.7546 | 109.5747 | 14.2503 | 5.7650 | 0.8972 | 3.1281 | 60.0000 | 70.9161 | 8.8647 | 1.2771 | 0.4264 | 0.6263 | 0.8973 | 0.6301 | 1.4698 | 0.3194 | 0.2748 | 0.0 | 0.0 | 0.0 | 4.8795 | 7.5418 | 10.0984 | 3.1182 | 15.0790 | 6.5280 | 0.0 | 2.8042 | 32.3594 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0301 | 21.3645 | 5.4178 | 9.3327 | 8.3977 | 148.0287 | 31.4674 | 45.5423 | 3.1842 | 13.3923 | 9.1221 | 0.0 | 2.6727 | 93.9245 | 0.0 | 434.2674 | 151.7665 | 0.0000 | 190.3869 | 746.9150 | 74.0741 | 191.7582 | 250.1742 | 34.1573 | 1.0281 | NaN | 3.9238 | 1.5357 | 10.8251 | 18.9849 | 9.0113 | 0.0 | 0.0000 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 240.7767 | 244.2748 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | 36.9067 | 2.9626 | 0.0 | 14.5293 | 0.0751 | 7.0870 | 12.1831 | 0.6451 | 6.4568 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1538 | 2.9667 | 9.3046 | 0.1096 | 0.0078 | 0.0026 | 7.116 | 0.7942 | 411.136 | 74.654 | 0.1832 | 16.16 | 0.85 | 0.0857 | 7.1619 | 0.3752 | 23.0713 | 3.9306 | 1.1386 | 1.5021 | 0.3718 | 0.1233 | 24.7524 | 267.064 | 0.9032 | 1.10 | 0.6219 | 0.4122 | 0.2562 | 0.4119 | 68.8489 | 535.0245 | 2.0293 | 11.21 | 0.1882 | 4.0923 | 0.0640 | 2.0952 | 9.2721 | 0.0584 | 0.0484 | 0.0148 | 82.8602 | 0.4958 | 0.0157 | 0.0039 | 3.1745 | 0.0584 | 0.0484 | 0.0148 | 82.8602 | 1 |
| 3 | 2008-07-19 14:43:00 | 2988.72 | 2479.90 | 2199.0333 | 909.7926 | 1.3204 | 100.0 | 104.2367 | 0.1217 | 1.4882 | -0.0124 | -0.0033 | 0.9629 | 201.8482 | 0.0 | 9.6052 | 422.2894 | 9.6924 | 0.9687 | 192.1557 | 12.4782 | 1.4011 | -5468.25 | 2648.25 | -4515.00 | -1657.25 | 1.3137 | 2.0038 | 7.3145 | 62.9333 | 2.6444 | 0.2071 | 3.3813 | 84.9105 | 8.6789 | 50.5100 | 64.1125 | 49.4900 | 65.1951 | 86.6867 | 117.0442 | 76.90 | 1.279 | 70.0 | 363.0273 | 9.9305 | 131.8027 | 733.8778 | 1.3027 | 142.5427 | 1.0 | 637.3727 | 189.9079 | 0.0 | 4.486 | 4.748 | 2936.0 | 0.9139 | 0.9447 | 4.5873 | 24.3791 | 361.4582 | 10.2112 | 116.1818 | 13.5597 | 15.6209 | 23.4736 | 710.4043 | 0.9761 | 147.6545 | 1.0 | 625.2945 | 70.2289 | 160.3210 | 464.9735 | 0.0 | -0.0555 | -0.0461 | -0.0400 | 0.0400 | 0.0676 | -0.1051 | 0.0028 | 0.0277 | 7.5925 | 0.1302 | NaN | 2.4004 | 0.9904 | 1752.0968 | 0.1958 | 8205.7000 | 0.0697 | -0.0003 | -0.0021 | -0.0001 | 0.0002 | 0.0411 | 0.0 | 0.0177 | -0.0195 | -0.0002 | 0.0000 | -0.0699 | -0.0059 | 0.0003 | 0.0003 | 0.0021 | -0.0483 | -0.1180 | NaN | NaN | NaN | 0.4647 | 0.9564 | 0.0 | 709.0867 | 0.9906 | 58.6635 | 0.6016 | 0.9761 | 6.4935 | 15.55 | 3.132 | 15.61 | 15.59 | 1.3660 | 2.480 | 0.5176 | 3.119 | 0.2838 | 0.7244 | 0.9961 | 2.3802 | 980.4510 | 41.1025 | 127.0 | 118.0 | 123.7 | 47.8000 | 162.4320 | 0.1915 | 0.0 | 5.51 | 0.0030 | 0.1140 | 0.0393 | 0.0613 | 0.0190 | 13.2651 | 0.0 | 9.073 | 15.241 | 1.3029 | 0.0150 | 11.9738 | 0.35 | 0.0699 | NaN | NaN | 859.0 | 355.0 | 3433.0 | 3004.0 | 0.068 | 0.108 | 0.100 | 1.7 | 0.9 | 0.086 | 0.241 | 0.9386 | 0.0356 | 0.2618 | 0.4391 | 0.2618 | 0.8567 | 0.2452 | 0.390 | 0.0 | 0.0 | 16.22 | 0.693 | 14.67 | 22.562 | 0.1786 | 5.69 | 0.0 | 18.20 | 52.571 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.139 | 5.92 | 23.60 | 1.264 | 10.63 | 13.56 | 5.92 | 11.382 | 24.320 | 0.3458 | 9.56 | 0.0 | 21.97 | 104.950 | 0.0 | 0.1248 | 0.0463 | 0.1223 | 0.0354 | 0.0708 | 0.0754 | 0.0643 | 0.0932 | 5.5398 | 0.0023 | NaN | 0.0764 | 0.0015 | 152.0885 | 0.0573 | 820.3999 | 0.0 | 0.0152 | 0.0149 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0067 | 0.0040 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | 0.0191 | 0.0234 | 0.0 | 94.0954 | 0.0010 | 3.2119 | 0.0406 | 0.0072 | 0.4212 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0513 | 31.830 | 3.1959 | 33.8960 | 37.8477 | 44.3906 | 16.9347 | 50.3631 | 0.0581 | 0.0 | 2.1775 | 0.0007 | 0.0417 | 0.0115 | 0.0172 | 0.0063 | 4.2154 | 0.0 | 2.8960 | 4.0526 | 0.3882 | 0.0049 | 3.9403 | 0.0916 | 0.0245 | NaN | NaN | 415.5048 | 157.0889 | 1572.6896 | 1377.4276 | 0.0285 | 0.0445 | 0.0465 | 0.6305 | 0.3046 | 0.0286 | 0.0824 | 0.3483 | 0.0128 | 0.1004 | 0.1701 | 0.1004 | 0.3465 | 0.0973 | 0.1675 | 0.0 | 0.0 | 0.0 | 5.4440 | 0.2004 | 4.1900 | 6.3329 | 0.0479 | 1.7339 | 0.0 | 4.9660 | 15.7375 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0243 | 1.7317 | 6.6262 | 0.3512 | 3.2699 | 9.4020 | 1.7317 | 3.0672 | 6.6839 | 0.0928 | 3.0229 | 0.0 | 6.3292 | 29.0339 | 8.4026 | 4.8851 | 0.0 | 0.0407 | 0.0198 | 0.0531 | 0.0167 | 0.0224 | 0.0422 | 0.0273 | 0.0484 | 1.8222 | 0.0006 | NaN | 0.0252 | 0.0004 | 45.7058 | 0.0188 | 309.8492 | 0.0 | 0.0046 | 0.0049 | 0.0028 | 0.0034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0024 | 0.0014 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | 0.0038 | 0.0068 | 0.0 | 32.4228 | 0.0003 | 1.1135 | 0.0132 | 0.0023 | 0.1348 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0155 | 13.3972 | 1.1907 | 5.6363 | 3.9482 | 4.9881 | 2.1737 | 17.8537 | 14.5054 | 0.0 | 5.2860 | 2.4643 | 7.6602 | 317.7362 | 0.0000 | 1.9689 | 6.5718 | 0.0 | 94.4594 | 3.6091 | 13.4420 | 1.5441 | 6.2313 | 2.8049 | 4.9898 | 15.7089 | 13.4051 | 76.0354 | 181.2641 | 5.1760 | 5.3899 | 1.3671 | 2.7013 | 34.0336 | 41.5236 | 7.1274 | 1.1054 | 0.4097 | 0.5183 | 0.6849 | 0.5290 | 1.3141 | 0.2829 | 0.3332 | 0.0 | 0.0 | 0.0 | 4.4680 | 6.9785 | 11.1303 | 3.0744 | 13.7105 | 3.9918 | 0.0 | 2.8555 | 27.6824 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0301 | 24.2831 | 6.5291 | 12.3786 | 9.1494 | 100.0021 | 37.8979 | 48.4887 | 3.4234 | 35.4323 | 6.4746 | 0.0 | 3.5135 | 149.4399 | 0.0 | 225.0169 | 100.4883 | 305.7500 | 88.5553 | 104.6660 | 71.7583 | 0.0000 | 336.7660 | 72.9635 | 1.7670 | NaN | 3.1817 | 0.1488 | 8.6804 | 29.2542 | 9.9979 | 0.0 | 0.0000 | 711.6418 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 113.5593 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | 4.1200 | 2.4416 | 0.0 | 13.2699 | 0.0977 | 5.4751 | 6.7553 | 0.7404 | 6.4865 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1565 | 3.2465 | 7.7754 | 0.1096 | 0.0078 | 0.0026 | 7.116 | 1.1650 | 372.822 | 72.442 | 1.8804 | 131.68 | 39.33 | 0.6812 | 56.9303 | 17.4781 | 161.4081 | 35.3198 | 54.2917 | 1.1613 | 0.7288 | 0.2710 | 62.7572 | 268.228 | 0.6511 | 7.32 | 0.1630 | 3.5611 | 0.0670 | 2.7290 | 25.0363 | 530.5682 | 2.0253 | 9.33 | 0.1738 | 2.8971 | 0.0525 | 1.7585 | 8.5831 | 0.0202 | 0.0149 | 0.0044 | 73.8432 | 0.4990 | 0.0103 | 0.0025 | 2.0544 | 0.0202 | 0.0149 | 0.0044 | 73.8432 | -1 |
| 4 | 2008-07-19 15:22:00 | 3032.24 | 2502.87 | 2233.3667 | 1326.5200 | 1.5334 | 100.0 | 100.3967 | 0.1235 | 1.5031 | -0.0031 | -0.0072 | 0.9569 | 201.9424 | 0.0 | 10.5661 | 420.5925 | 10.3387 | 0.9735 | 191.6037 | 12.4735 | 1.3888 | -5476.25 | 2635.25 | -3987.50 | 117.00 | 1.2887 | 1.9912 | 7.2748 | 62.8333 | 3.1556 | 0.2696 | 3.2728 | 86.3269 | 8.7677 | 50.2480 | 64.1511 | 49.7520 | 66.1542 | 86.1468 | 121.4364 | 76.39 | 2.209 | 70.0 | 353.3400 | 10.4091 | 176.3136 | 789.7523 | 1.0341 | 138.0882 | 1.0 | 667.7418 | 233.5491 | 0.0 | 4.624 | 4.894 | 2865.0 | 0.9298 | 0.9449 | 4.6414 | -12.2945 | 355.0809 | 9.7948 | 144.0191 | 21.9782 | 32.2945 | 44.1498 | 745.6025 | 0.9256 | 146.6636 | 1.0 | 645.7636 | 65.8417 | NaN | NaN | 0.0 | -0.0534 | 0.0183 | -0.0167 | -0.0449 | 0.0034 | -0.0178 | -0.0123 | -0.0048 | 7.5017 | 0.1342 | NaN | 2.4530 | 0.9902 | 1828.3846 | 0.1829 | 9014.4600 | 0.0448 | -0.0077 | -0.0001 | -0.0001 | -0.0001 | 0.2189 | 0.0 | -0.6704 | -0.0167 | 0.0004 | -0.0003 | 0.0696 | -0.0045 | 0.0002 | 0.0078 | 0.0000 | -0.0799 | -0.2038 | NaN | NaN | NaN | NaN | 0.9424 | 0.0 | 796.5950 | 0.9908 | 58.3858 | 0.5913 | 0.9628 | 6.3551 | 15.75 | 3.148 | 15.73 | 15.71 | 0.9460 | 3.027 | 0.5328 | 3.299 | -0.5677 | 0.7780 | 1.0010 | 2.3715 | 993.1274 | 38.1448 | 119.0 | 143.2 | 123.1 | 48.8000 | 296.3030 | 0.3744 | 0.0 | 3.64 | 0.0041 | 0.0634 | 0.0451 | 0.0623 | 0.0240 | 14.2354 | 0.0 | 9.005 | 12.506 | 0.4434 | 0.0126 | 13.9047 | 0.43 | 0.0538 | NaN | NaN | 699.0 | 283.0 | 1747.0 | 1443.0 | 0.147 | 0.040 | 0.113 | 3.9 | 0.8 | 0.101 | 0.499 | 0.5760 | 0.0631 | 0.3053 | 0.5830 | 0.3053 | 0.8285 | 0.1308 | 0.922 | 0.0 | 0.0 | 15.24 | 0.282 | 10.85 | 37.715 | 0.1189 | 3.98 | 0.0 | 25.54 | 72.149 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.250 | 5.52 | 15.76 | 0.519 | 10.71 | 19.77 | 5.52 | 8.446 | 33.832 | 0.3951 | 9.09 | 0.0 | 19.77 | 92.307 | 0.0 | 0.0915 | 0.0506 | 0.0769 | 0.1079 | 0.0797 | 0.1047 | 0.0924 | 0.1015 | 4.1338 | 0.0030 | NaN | 0.0802 | 0.0004 | 69.1510 | 0.1970 | 1406.4004 | 0.0 | 0.0227 | 0.0272 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0067 | 0.0031 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | 0.0240 | 0.0 | 149.2172 | 0.0006 | 2.5775 | 0.0177 | 0.0214 | 0.4051 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0488 | 19.862 | 3.6163 | 34.1250 | 55.9626 | 53.0876 | 17.4864 | 88.7672 | 0.1092 | 0.0 | 1.0929 | 0.0013 | 0.0257 | 0.0116 | 0.0163 | 0.0080 | 4.4239 | 0.0 | 3.2376 | 3.6536 | 0.1293 | 0.0040 | 4.3474 | 0.1275 | 0.0181 | NaN | NaN | 319.1252 | 128.0296 | 799.5884 | 628.3083 | 0.0755 | 0.0181 | 0.0476 | 1.3500 | 0.2698 | 0.0320 | 0.1541 | 0.2155 | 0.0310 | 0.1354 | 0.2194 | 0.1354 | 0.3072 | 0.0582 | 0.3574 | 0.0 | 0.0 | 0.0 | 4.8956 | 0.0766 | 2.9130 | 11.0583 | 0.0327 | 1.1229 | 0.0 | 7.3296 | 23.1160 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0822 | 1.6216 | 4.7279 | 0.1773 | 3.1550 | 9.7777 | 1.6216 | 2.5923 | 10.5352 | 0.1301 | 3.0939 | 0.0 | 6.3767 | 32.0537 | NaN | NaN | 0.0 | 0.0246 | 0.0221 | 0.0329 | 0.0522 | 0.0256 | 0.0545 | 0.0476 | 0.0463 | 1.5530 | 0.0010 | NaN | 0.0286 | 0.0001 | 21.0312 | 0.0573 | 494.7368 | 0.0 | 0.0063 | 0.0077 | 0.0052 | 0.0027 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0025 | 0.0012 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | 0.0089 | 0.0 | 57.2692 | 0.0002 | 0.8495 | 0.0065 | 0.0077 | 0.1356 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0165 | 7.1493 | 1.1704 | 5.3823 | 4.7226 | 4.9184 | 2.1850 | 22.3369 | 24.4142 | 0.0 | 3.6256 | 3.3208 | 4.2178 | 0.0000 | 866.0295 | 2.5046 | 7.0492 | 0.0 | 85.2255 | 2.9734 | 4.2892 | 1.2943 | 7.2570 | 3.4473 | 3.8754 | 12.7642 | 10.7390 | 43.8119 | 0.0000 | 11.4064 | 2.0088 | 1.5533 | 6.2069 | 25.3521 | 37.4691 | 15.2470 | 0.6672 | 0.7198 | 0.6076 | 0.9088 | 0.6136 | 1.2524 | 0.1518 | 0.7592 | 0.0 | 0.0 | 0.0 | 4.3131 | 2.7092 | 6.1538 | 4.7756 | 11.4945 | 2.8822 | 0.0 | 3.8248 | 30.8924 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5.3863 | 44.8980 | 4.4384 | 5.2987 | 7.4365 | 89.9529 | 17.0927 | 19.1303 | 4.5375 | 42.6838 | 6.1979 | 0.0 | 3.0615 | 140.1953 | 0.0 | 171.4486 | 276.8810 | 461.8619 | 240.1781 | 0.0000 | 587.3773 | 748.1781 | 0.0000 | 55.1057 | 2.2358 | NaN | 3.2712 | 0.0372 | 3.7821 | 107.6905 | 15.6016 | 0.0 | 293.1396 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 148.0663 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | NaN | NaN | NaN | 2.5512 | 0.0 | 18.7319 | 0.0616 | 4.4146 | 2.9954 | 2.2181 | 6.3745 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0579 | 1.9999 | 9.4805 | 0.1096 | 0.0078 | 0.0026 | 7.116 | 1.4636 | 399.914 | 79.156 | 1.0388 | 19.63 | 1.98 | 0.4287 | 9.7608 | 0.8311 | 70.9706 | 4.9086 | 2.5014 | 0.9778 | 0.2156 | 0.0461 | 22.0500 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 532.0155 | 2.0275 | 8.83 | 0.2224 | 3.1776 | 0.0706 | 1.6597 | 10.9698 | NaN | NaN | NaN | NaN | 0.4800 | 0.4766 | 0.1045 | 99.3032 | 0.0202 | 0.0149 | 0.0044 | 73.8432 | -1 |
print('''\n\033[1m''' + '''Checking unique data-types of Dataset''' + '''\033[0m''')
print(sd.dtypes.unique())
Checking unique data-types of Dataset
[dtype('O') dtype('float64') dtype('int64')]
print('''\n\033[1m''' + '''Getting data-type of each column of data set with thier length''' + '''\033[0m''')
Float=sd.select_dtypes(include='float64').columns
Int=sd.select_dtypes(include='int64').columns
Obj=sd.select_dtypes(include='object').columns
print('\nInteger type columns are\n',Int,'of Total',len(Int),'\n')
print('Float type of columns are\n',Float,'of Total',len(Float),'\n')
print('Object type columns are\n',Obj,'of Total',len(Obj),'\n')
Getting data-type of each column of data set with thier length
Integer type columns are
Index(['Pass/Fail'], dtype='object') of Total 1
Float type of columns are
Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
...
'580', '581', '582', '583', '584', '585', '586', '587', '588', '589'],
dtype='object', length=590) of Total 590
Object type columns are
Index(['Time'], dtype='object') of Total 1
print('''\n\033[1m''' + '''Getting shape of the data''' + '''\033[0m''')
print('The data contains \nNo. of rows = ',sd.shape[0],'\nNo. of columns = ',sd.shape[1])
Getting shape of the data
The data contains
No. of rows = 1567
No. of columns = 592
print('''\n\033[1m''' + '''Checking total number of missing values in each column:''' + '''\033[0m''')
for x in sd:
i=sd[sd[x].isnull()].index.tolist()
s = sd[x].isnull().sum()
if s!=0:
print('Column',x,'has total missing values of',len(i),'\n')
Checking total number of missing values in each column:
Column 0 has total missing values of 6
Column 1 has total missing values of 7
Column 2 has total missing values of 14
Column 3 has total missing values of 14
Column 4 has total missing values of 14
Column 5 has total missing values of 14
Column 6 has total missing values of 14
Column 7 has total missing values of 9
Column 8 has total missing values of 2
Column 9 has total missing values of 2
Column 10 has total missing values of 2
Column 11 has total missing values of 2
Column 12 has total missing values of 2
Column 13 has total missing values of 3
Column 14 has total missing values of 3
Column 15 has total missing values of 3
Column 16 has total missing values of 3
Column 17 has total missing values of 3
Column 18 has total missing values of 3
Column 19 has total missing values of 10
Column 21 has total missing values of 2
Column 22 has total missing values of 2
Column 23 has total missing values of 2
Column 24 has total missing values of 2
Column 25 has total missing values of 2
Column 26 has total missing values of 2
Column 27 has total missing values of 2
Column 28 has total missing values of 2
Column 29 has total missing values of 2
Column 30 has total missing values of 2
Column 31 has total missing values of 2
Column 32 has total missing values of 1
Column 33 has total missing values of 1
Column 34 has total missing values of 1
Column 35 has total missing values of 1
Column 36 has total missing values of 1
Column 37 has total missing values of 1
Column 38 has total missing values of 1
Column 39 has total missing values of 1
Column 40 has total missing values of 24
Column 41 has total missing values of 24
Column 42 has total missing values of 1
Column 43 has total missing values of 1
Column 44 has total missing values of 1
Column 45 has total missing values of 1
Column 46 has total missing values of 1
Column 47 has total missing values of 1
Column 48 has total missing values of 1
Column 49 has total missing values of 1
Column 50 has total missing values of 1
Column 51 has total missing values of 1
Column 52 has total missing values of 1
Column 53 has total missing values of 4
Column 54 has total missing values of 4
Column 55 has total missing values of 4
Column 56 has total missing values of 4
Column 57 has total missing values of 4
Column 58 has total missing values of 4
Column 59 has total missing values of 7
Column 60 has total missing values of 6
Column 61 has total missing values of 6
Column 62 has total missing values of 6
Column 63 has total missing values of 7
Column 64 has total missing values of 7
Column 65 has total missing values of 7
Column 66 has total missing values of 6
Column 67 has total missing values of 6
Column 68 has total missing values of 6
Column 69 has total missing values of 6
Column 70 has total missing values of 6
Column 71 has total missing values of 6
Column 72 has total missing values of 794
Column 73 has total missing values of 794
Column 74 has total missing values of 6
Column 75 has total missing values of 24
Column 76 has total missing values of 24
Column 77 has total missing values of 24
Column 78 has total missing values of 24
Column 79 has total missing values of 24
Column 80 has total missing values of 24
Column 81 has total missing values of 24
Column 82 has total missing values of 24
Column 83 has total missing values of 1
Column 84 has total missing values of 12
Column 85 has total missing values of 1341
Column 89 has total missing values of 51
Column 90 has total missing values of 51
Column 91 has total missing values of 6
Column 92 has total missing values of 2
Column 93 has total missing values of 2
Column 94 has total missing values of 6
Column 95 has total missing values of 6
Column 96 has total missing values of 6
Column 97 has total missing values of 6
Column 98 has total missing values of 6
Column 99 has total missing values of 6
Column 100 has total missing values of 6
Column 101 has total missing values of 6
Column 102 has total missing values of 6
Column 103 has total missing values of 2
Column 104 has total missing values of 2
Column 105 has total missing values of 6
Column 106 has total missing values of 6
Column 107 has total missing values of 6
Column 108 has total missing values of 6
Column 109 has total missing values of 1018
Column 110 has total missing values of 1018
Column 111 has total missing values of 1018
Column 112 has total missing values of 715
Column 118 has total missing values of 24
Column 121 has total missing values of 9
Column 122 has total missing values of 9
Column 123 has total missing values of 9
Column 124 has total missing values of 9
Column 125 has total missing values of 9
Column 126 has total missing values of 9
Column 127 has total missing values of 9
Column 128 has total missing values of 9
Column 129 has total missing values of 9
Column 130 has total missing values of 9
Column 131 has total missing values of 9
Column 132 has total missing values of 8
Column 133 has total missing values of 8
Column 134 has total missing values of 8
Column 135 has total missing values of 5
Column 136 has total missing values of 6
Column 137 has total missing values of 7
Column 138 has total missing values of 14
Column 139 has total missing values of 14
Column 140 has total missing values of 14
Column 141 has total missing values of 14
Column 142 has total missing values of 14
Column 143 has total missing values of 9
Column 144 has total missing values of 2
Column 145 has total missing values of 2
Column 146 has total missing values of 2
Column 147 has total missing values of 2
Column 148 has total missing values of 2
Column 149 has total missing values of 3
Column 150 has total missing values of 3
Column 151 has total missing values of 3
Column 152 has total missing values of 3
Column 153 has total missing values of 3
Column 154 has total missing values of 3
Column 155 has total missing values of 10
Column 157 has total missing values of 1429
Column 158 has total missing values of 1429
Column 159 has total missing values of 2
Column 160 has total missing values of 2
Column 161 has total missing values of 2
Column 162 has total missing values of 2
Column 163 has total missing values of 2
Column 164 has total missing values of 2
Column 165 has total missing values of 2
Column 166 has total missing values of 2
Column 167 has total missing values of 2
Column 168 has total missing values of 2
Column 169 has total missing values of 2
Column 170 has total missing values of 1
Column 171 has total missing values of 1
Column 172 has total missing values of 1
Column 173 has total missing values of 1
Column 174 has total missing values of 1
Column 175 has total missing values of 1
Column 176 has total missing values of 1
Column 177 has total missing values of 1
Column 178 has total missing values of 24
Column 179 has total missing values of 1
Column 180 has total missing values of 1
Column 181 has total missing values of 1
Column 182 has total missing values of 1
Column 183 has total missing values of 1
Column 184 has total missing values of 1
Column 185 has total missing values of 1
Column 186 has total missing values of 1
Column 187 has total missing values of 1
Column 188 has total missing values of 1
Column 189 has total missing values of 1
Column 190 has total missing values of 4
Column 191 has total missing values of 4
Column 192 has total missing values of 4
Column 193 has total missing values of 4
Column 194 has total missing values of 4
Column 195 has total missing values of 4
Column 196 has total missing values of 7
Column 197 has total missing values of 6
Column 198 has total missing values of 6
Column 199 has total missing values of 6
Column 200 has total missing values of 7
Column 201 has total missing values of 7
Column 202 has total missing values of 7
Column 203 has total missing values of 6
Column 204 has total missing values of 6
Column 205 has total missing values of 6
Column 206 has total missing values of 6
Column 207 has total missing values of 6
Column 208 has total missing values of 6
Column 209 has total missing values of 6
Column 210 has total missing values of 24
Column 211 has total missing values of 24
Column 212 has total missing values of 24
Column 213 has total missing values of 24
Column 214 has total missing values of 24
Column 215 has total missing values of 24
Column 216 has total missing values of 24
Column 217 has total missing values of 24
Column 218 has total missing values of 1
Column 219 has total missing values of 12
Column 220 has total missing values of 1341
Column 224 has total missing values of 51
Column 225 has total missing values of 51
Column 226 has total missing values of 6
Column 227 has total missing values of 2
Column 228 has total missing values of 2
Column 229 has total missing values of 6
Column 230 has total missing values of 6
Column 231 has total missing values of 6
Column 232 has total missing values of 6
Column 233 has total missing values of 6
Column 234 has total missing values of 6
Column 235 has total missing values of 6
Column 236 has total missing values of 6
Column 237 has total missing values of 6
Column 238 has total missing values of 2
Column 239 has total missing values of 2
Column 240 has total missing values of 6
Column 241 has total missing values of 6
Column 242 has total missing values of 6
Column 243 has total missing values of 6
Column 244 has total missing values of 1018
Column 245 has total missing values of 1018
Column 246 has total missing values of 1018
Column 247 has total missing values of 715
Column 253 has total missing values of 24
Column 256 has total missing values of 9
Column 257 has total missing values of 9
Column 258 has total missing values of 9
Column 259 has total missing values of 9
Column 260 has total missing values of 9
Column 261 has total missing values of 9
Column 262 has total missing values of 9
Column 263 has total missing values of 9
Column 264 has total missing values of 9
Column 265 has total missing values of 9
Column 266 has total missing values of 9
Column 267 has total missing values of 8
Column 268 has total missing values of 8
Column 269 has total missing values of 8
Column 270 has total missing values of 5
Column 271 has total missing values of 6
Column 272 has total missing values of 7
Column 273 has total missing values of 14
Column 274 has total missing values of 14
Column 275 has total missing values of 14
Column 276 has total missing values of 14
Column 277 has total missing values of 14
Column 278 has total missing values of 9
Column 279 has total missing values of 2
Column 280 has total missing values of 2
Column 281 has total missing values of 2
Column 282 has total missing values of 2
Column 283 has total missing values of 2
Column 284 has total missing values of 3
Column 285 has total missing values of 3
Column 286 has total missing values of 3
Column 287 has total missing values of 3
Column 288 has total missing values of 3
Column 289 has total missing values of 3
Column 290 has total missing values of 10
Column 292 has total missing values of 1429
Column 293 has total missing values of 1429
Column 294 has total missing values of 2
Column 295 has total missing values of 2
Column 296 has total missing values of 2
Column 297 has total missing values of 2
Column 298 has total missing values of 2
Column 299 has total missing values of 2
Column 300 has total missing values of 2
Column 301 has total missing values of 2
Column 302 has total missing values of 2
Column 303 has total missing values of 2
Column 304 has total missing values of 2
Column 305 has total missing values of 1
Column 306 has total missing values of 1
Column 307 has total missing values of 1
Column 308 has total missing values of 1
Column 309 has total missing values of 1
Column 310 has total missing values of 1
Column 311 has total missing values of 1
Column 312 has total missing values of 1
Column 313 has total missing values of 24
Column 314 has total missing values of 24
Column 315 has total missing values of 1
Column 316 has total missing values of 1
Column 317 has total missing values of 1
Column 318 has total missing values of 1
Column 319 has total missing values of 1
Column 320 has total missing values of 1
Column 321 has total missing values of 1
Column 322 has total missing values of 1
Column 323 has total missing values of 1
Column 324 has total missing values of 1
Column 325 has total missing values of 1
Column 326 has total missing values of 4
Column 327 has total missing values of 4
Column 328 has total missing values of 4
Column 329 has total missing values of 4
Column 330 has total missing values of 4
Column 331 has total missing values of 4
Column 332 has total missing values of 7
Column 333 has total missing values of 6
Column 334 has total missing values of 6
Column 335 has total missing values of 6
Column 336 has total missing values of 7
Column 337 has total missing values of 7
Column 338 has total missing values of 7
Column 339 has total missing values of 6
Column 340 has total missing values of 6
Column 341 has total missing values of 6
Column 342 has total missing values of 6
Column 343 has total missing values of 6
Column 344 has total missing values of 6
Column 345 has total missing values of 794
Column 346 has total missing values of 794
Column 347 has total missing values of 6
Column 348 has total missing values of 24
Column 349 has total missing values of 24
Column 350 has total missing values of 24
Column 351 has total missing values of 24
Column 352 has total missing values of 24
Column 353 has total missing values of 24
Column 354 has total missing values of 24
Column 355 has total missing values of 24
Column 356 has total missing values of 1
Column 357 has total missing values of 12
Column 358 has total missing values of 1341
Column 362 has total missing values of 51
Column 363 has total missing values of 51
Column 364 has total missing values of 6
Column 365 has total missing values of 2
Column 366 has total missing values of 2
Column 367 has total missing values of 6
Column 368 has total missing values of 6
Column 369 has total missing values of 6
Column 370 has total missing values of 6
Column 371 has total missing values of 6
Column 372 has total missing values of 6
Column 373 has total missing values of 6
Column 374 has total missing values of 6
Column 375 has total missing values of 6
Column 376 has total missing values of 2
Column 377 has total missing values of 2
Column 378 has total missing values of 6
Column 379 has total missing values of 6
Column 380 has total missing values of 6
Column 381 has total missing values of 6
Column 382 has total missing values of 1018
Column 383 has total missing values of 1018
Column 384 has total missing values of 1018
Column 385 has total missing values of 715
Column 391 has total missing values of 24
Column 394 has total missing values of 9
Column 395 has total missing values of 9
Column 396 has total missing values of 9
Column 397 has total missing values of 9
Column 398 has total missing values of 9
Column 399 has total missing values of 9
Column 400 has total missing values of 9
Column 401 has total missing values of 9
Column 402 has total missing values of 9
Column 403 has total missing values of 9
Column 404 has total missing values of 9
Column 405 has total missing values of 8
Column 406 has total missing values of 8
Column 407 has total missing values of 8
Column 408 has total missing values of 5
Column 409 has total missing values of 6
Column 410 has total missing values of 7
Column 411 has total missing values of 14
Column 412 has total missing values of 14
Column 413 has total missing values of 14
Column 414 has total missing values of 14
Column 415 has total missing values of 14
Column 416 has total missing values of 9
Column 417 has total missing values of 2
Column 418 has total missing values of 2
Column 419 has total missing values of 2
Column 420 has total missing values of 2
Column 421 has total missing values of 2
Column 422 has total missing values of 3
Column 423 has total missing values of 3
Column 424 has total missing values of 3
Column 425 has total missing values of 3
Column 426 has total missing values of 3
Column 427 has total missing values of 3
Column 428 has total missing values of 10
Column 430 has total missing values of 2
Column 431 has total missing values of 2
Column 432 has total missing values of 2
Column 433 has total missing values of 2
Column 434 has total missing values of 2
Column 435 has total missing values of 2
Column 436 has total missing values of 2
Column 437 has total missing values of 2
Column 438 has total missing values of 2
Column 439 has total missing values of 2
Column 440 has total missing values of 2
Column 441 has total missing values of 1
Column 442 has total missing values of 1
Column 443 has total missing values of 1
Column 444 has total missing values of 1
Column 445 has total missing values of 1
Column 446 has total missing values of 1
Column 447 has total missing values of 1
Column 448 has total missing values of 1
Column 449 has total missing values of 24
Column 450 has total missing values of 24
Column 451 has total missing values of 1
Column 452 has total missing values of 1
Column 453 has total missing values of 1
Column 454 has total missing values of 1
Column 455 has total missing values of 1
Column 456 has total missing values of 1
Column 457 has total missing values of 1
Column 458 has total missing values of 1
Column 459 has total missing values of 1
Column 460 has total missing values of 1
Column 461 has total missing values of 1
Column 462 has total missing values of 4
Column 463 has total missing values of 4
Column 464 has total missing values of 4
Column 465 has total missing values of 4
Column 466 has total missing values of 4
Column 467 has total missing values of 4
Column 468 has total missing values of 7
Column 469 has total missing values of 6
Column 470 has total missing values of 6
Column 471 has total missing values of 6
Column 472 has total missing values of 7
Column 473 has total missing values of 7
Column 474 has total missing values of 7
Column 475 has total missing values of 6
Column 476 has total missing values of 6
Column 477 has total missing values of 6
Column 478 has total missing values of 6
Column 479 has total missing values of 6
Column 480 has total missing values of 6
Column 481 has total missing values of 6
Column 482 has total missing values of 24
Column 483 has total missing values of 24
Column 484 has total missing values of 24
Column 485 has total missing values of 24
Column 486 has total missing values of 24
Column 487 has total missing values of 24
Column 488 has total missing values of 24
Column 489 has total missing values of 24
Column 490 has total missing values of 1
Column 491 has total missing values of 12
Column 492 has total missing values of 1341
Column 496 has total missing values of 51
Column 497 has total missing values of 51
Column 498 has total missing values of 6
Column 499 has total missing values of 2
Column 500 has total missing values of 2
Column 501 has total missing values of 6
Column 502 has total missing values of 6
Column 503 has total missing values of 6
Column 504 has total missing values of 6
Column 505 has total missing values of 6
Column 506 has total missing values of 6
Column 507 has total missing values of 6
Column 508 has total missing values of 6
Column 509 has total missing values of 6
Column 510 has total missing values of 2
Column 511 has total missing values of 2
Column 512 has total missing values of 6
Column 513 has total missing values of 6
Column 514 has total missing values of 6
Column 515 has total missing values of 6
Column 516 has total missing values of 1018
Column 517 has total missing values of 1018
Column 518 has total missing values of 1018
Column 519 has total missing values of 715
Column 525 has total missing values of 24
Column 528 has total missing values of 9
Column 529 has total missing values of 9
Column 530 has total missing values of 9
Column 531 has total missing values of 9
Column 532 has total missing values of 9
Column 533 has total missing values of 9
Column 534 has total missing values of 9
Column 535 has total missing values of 9
Column 536 has total missing values of 9
Column 537 has total missing values of 9
Column 538 has total missing values of 9
Column 539 has total missing values of 8
Column 540 has total missing values of 8
Column 541 has total missing values of 8
Column 542 has total missing values of 2
Column 543 has total missing values of 2
Column 544 has total missing values of 2
Column 545 has total missing values of 2
Column 546 has total missing values of 260
Column 547 has total missing values of 260
Column 548 has total missing values of 260
Column 549 has total missing values of 260
Column 550 has total missing values of 260
Column 551 has total missing values of 260
Column 552 has total missing values of 260
Column 553 has total missing values of 260
Column 554 has total missing values of 260
Column 555 has total missing values of 260
Column 556 has total missing values of 260
Column 557 has total missing values of 260
Column 558 has total missing values of 1
Column 559 has total missing values of 1
Column 560 has total missing values of 1
Column 561 has total missing values of 1
Column 562 has total missing values of 273
Column 563 has total missing values of 273
Column 564 has total missing values of 273
Column 565 has total missing values of 273
Column 566 has total missing values of 273
Column 567 has total missing values of 273
Column 568 has total missing values of 273
Column 569 has total missing values of 273
Column 578 has total missing values of 949
Column 579 has total missing values of 949
Column 580 has total missing values of 949
Column 581 has total missing values of 949
Column 582 has total missing values of 1
Column 583 has total missing values of 1
Column 584 has total missing values of 1
Column 585 has total missing values of 1
Column 586 has total missing values of 1
Column 587 has total missing values of 1
Column 588 has total missing values of 1
Column 589 has total missing values of 1
print('''\n\033[1m''' + '''Missing values with more than below value can be droped''' + '''\033[0m''')
sd.shape[0]/2
Missing values with more than below value can be droped
783.5
ulf=[]
ulf
[]
print('''\n\033[1m''' + '''Removing columns with missing values more than 784:\n''' + '''\033[0m''')
co=[]
for x in sd:
i=sd[sd[x].isnull()].index.tolist()
s = sd[x].isnull().sum()
if s!=0:
c=len(i)
if c >= 784:
print('Column',x,'has total missing values',c,'which is more than 784')
print('Therefore Removing',x,'column\n')
ulf.append(x)
sd=sd.drop(x,axis=1)
Removing columns with missing values more than 784:
Column 72 has total missing values 794 which is more than 784
Therefore Removing 72 column
Column 73 has total missing values 794 which is more than 784
Therefore Removing 73 column
Column 85 has total missing values 1341 which is more than 784
Therefore Removing 85 column
Column 109 has total missing values 1018 which is more than 784
Therefore Removing 109 column
Column 110 has total missing values 1018 which is more than 784
Therefore Removing 110 column
Column 111 has total missing values 1018 which is more than 784
Therefore Removing 111 column
Column 157 has total missing values 1429 which is more than 784
Therefore Removing 157 column
Column 158 has total missing values 1429 which is more than 784
Therefore Removing 158 column
Column 220 has total missing values 1341 which is more than 784
Therefore Removing 220 column
Column 244 has total missing values 1018 which is more than 784
Therefore Removing 244 column
Column 245 has total missing values 1018 which is more than 784
Therefore Removing 245 column
Column 246 has total missing values 1018 which is more than 784
Therefore Removing 246 column
Column 292 has total missing values 1429 which is more than 784
Therefore Removing 292 column
Column 293 has total missing values 1429 which is more than 784
Therefore Removing 293 column
Column 345 has total missing values 794 which is more than 784
Therefore Removing 345 column
Column 346 has total missing values 794 which is more than 784
Therefore Removing 346 column
Column 358 has total missing values 1341 which is more than 784
Therefore Removing 358 column
Column 382 has total missing values 1018 which is more than 784
Therefore Removing 382 column
Column 383 has total missing values 1018 which is more than 784
Therefore Removing 383 column
Column 384 has total missing values 1018 which is more than 784
Therefore Removing 384 column
Column 492 has total missing values 1341 which is more than 784
Therefore Removing 492 column
Column 516 has total missing values 1018 which is more than 784
Therefore Removing 516 column
Column 517 has total missing values 1018 which is more than 784
Therefore Removing 517 column
Column 518 has total missing values 1018 which is more than 784
Therefore Removing 518 column
Column 578 has total missing values 949 which is more than 784
Therefore Removing 578 column
Column 579 has total missing values 949 which is more than 784
Therefore Removing 579 column
Column 580 has total missing values 949 which is more than 784
Therefore Removing 580 column
Column 581 has total missing values 949 which is more than 784
Therefore Removing 581 column
print('''\n\033[1m''' + '''Replacing other columns by their median value:''' + '''\033[0m''')
for x in sd:
s = sd[x].isnull().sum()
if s!=0:
median=sd[x].median()
print('Median of',x,median)
sd[x].fillna(median,inplace=True)
Replacing other columns by their median value:
Median of 0 3011.49
Median of 1 2499.4049999999997
Median of 2 2201.0667
Median of 3 1285.2144
Median of 4 1.3168
Median of 5 100.0
Median of 6 101.5122
Median of 7 0.1224
Median of 8 1.4616
Median of 9 -0.0013
Median of 10 0.0004
Median of 11 0.9658
Median of 12 199.5356
Median of 13 0.0
Median of 14 8.966999999999999
Median of 15 412.2191
Median of 16 9.85175
Median of 17 0.9726
Median of 18 189.6642
Median of 19 12.4996
Median of 21 -5523.25
Median of 22 2664.0
Median of 23 -3820.75
Median of 24 -78.75
Median of 25 1.283
Median of 26 1.9865
Median of 27 7.2647
Median of 28 69.1556
Median of 29 2.3778
Median of 30 0.1867
Median of 31 3.431
Median of 32 85.13544999999999
Median of 33 8.7698
Median of 34 50.3964
Median of 35 64.1658
Median of 36 49.6036
Median of 37 66.23179999999999
Median of 38 86.8207
Median of 39 118.39930000000001
Median of 40 78.29
Median of 41 3.074
Median of 42 70.0
Median of 43 353.72090000000003
Median of 44 10.03485
Median of 45 136.4
Median of 46 733.45
Median of 47 1.25105
Median of 48 140.00775
Median of 49 1.0
Median of 50 631.3709
Median of 51 183.31815
Median of 52 0.0
Median of 53 4.596
Median of 54 4.843
Median of 55 2854.0
Median of 56 0.931
Median of 57 0.9493
Median of 58 4.5727
Median of 59 0.9472499999999999
Median of 60 353.7991
Median of 61 10.4367
Median of 62 116.2118
Median of 63 13.24605
Median of 64 20.021349999999998
Median of 65 26.26145
Median of 66 706.4536
Median of 67 0.9783
Median of 68 147.5973
Median of 69 1.0
Median of 70 619.0327
Median of 71 102.6043
Median of 74 0.0
Median of 75 -0.0063
Median of 76 -0.0289
Median of 77 -0.0099
Median of 78 -0.0125
Median of 79 0.0006
Median of 80 -0.0087
Median of 81 -0.0196
Median of 82 0.0076
Median of 83 7.4674499999999995
Median of 84 0.133
Median of 89 0.1901
Median of 90 8825.435099999999
Median of 91 0.0
Median of 92 0.0004
Median of 93 -0.0002
Median of 94 0.0
Median of 95 0.0
Median of 96 0.0039
Median of 97 0.0
Median of 98 0.0
Median of 99 0.0
Median of 100 0.0
Median of 101 0.0
Median of 102 0.0
Median of 103 -0.0101
Median of 104 0.0
Median of 105 -0.0002
Median of 106 0.0002
Median of 107 0.0
Median of 108 -0.0112
Median of 112 0.46285
Median of 118 0.599
Median of 121 15.79
Median of 122 3.877
Median of 123 15.83
Median of 124 15.78
Median of 125 1.144
Median of 126 2.735
Median of 127 0.6539
Median of 128 3.195
Median of 129 -0.1419
Median of 130 0.75875
Median of 131 0.99775
Median of 132 2.3124
Median of 133 1004.05
Median of 134 38.9026
Median of 135 109.0
Median of 136 134.6
Median of 137 117.7
Median of 138 55.9001
Median of 139 339.561
Median of 140 0.2358
Median of 141 0.0
Median of 142 6.26
Median of 143 0.0039
Median of 144 0.1075
Median of 145 0.0586
Median of 146 0.05
Median of 147 0.0159
Median of 148 7.9173
Median of 149 0.0
Median of 150 5.950999999999999
Median of 151 10.993500000000001
Median of 152 0.4687
Median of 153 0.0111
Median of 154 7.5127
Median of 155 0.32
Median of 159 623.0
Median of 160 438.0
Median of 161 2614.0
Median of 162 1784.0
Median of 163 0.12
Median of 164 0.08900000000000001
Median of 165 0.184
Median of 166 2.6
Median of 167 1.2
Median of 168 0.119
Median of 169 0.412
Median of 170 0.6859999999999999
Median of 171 0.1125
Median of 172 0.32384999999999997
Median of 173 0.5776
Median of 174 0.32384999999999997
Median of 175 0.7682
Median of 176 0.2429
Median of 177 0.299
Median of 178 0.0
Median of 179 0.0
Median of 180 18.69
Median of 181 0.524
Median of 182 10.17
Median of 183 27.200499999999998
Median of 184 0.1326
Median of 185 6.735
Median of 186 0.0
Median of 187 17.865000000000002
Median of 188 40.209500000000006
Median of 189 0.0
Median of 190 0.0
Median of 191 0.0
Median of 192 0.0
Median of 193 0.0
Median of 194 0.0
Median of 195 0.259
Median of 196 6.78
Median of 197 19.37
Median of 198 0.424
Median of 199 8.57
Median of 200 17.235
Median of 201 6.76
Median of 202 8.462
Median of 203 30.097
Median of 204 0.1582
Median of 205 7.74
Median of 206 0.0
Median of 207 19.72
Median of 208 73.248
Median of 209 0.0
Median of 210 0.0797
Median of 211 0.0532
Median of 212 0.0416
Median of 213 0.056
Median of 214 0.0754
Median of 215 0.0825
Median of 216 0.0846
Median of 217 0.0617
Median of 218 3.63075
Median of 219 0.003
Median of 224 0.0398
Median of 225 967.2998
Median of 226 0.0
Median of 227 0.0165
Median of 228 0.0155
Median of 229 0.0
Median of 230 0.0
Median of 231 0.0
Median of 232 0.0
Median of 233 0.0
Median of 234 0.0
Median of 235 0.0
Median of 236 0.0
Median of 237 0.0
Median of 238 0.0046
Median of 239 0.0044
Median of 240 0.0
Median of 241 0.0
Median of 242 0.0
Median of 243 0.0
Median of 247 0.027
Median of 253 0.0308
Median of 256 0.0
Median of 257 0.0
Median of 258 0.0
Median of 259 0.0
Median of 260 0.0
Median of 261 0.0
Median of 262 0.0
Median of 263 0.0
Median of 264 0.0
Median of 265 0.0
Median of 266 0.0
Median of 267 0.0706
Median of 268 17.977
Median of 269 3.7035
Median of 270 28.7735
Median of 271 45.6765
Median of 272 40.01925
Median of 273 19.5809
Median of 274 110.6014
Median of 275 0.0784
Median of 276 0.0
Median of 277 2.0831
Median of 278 0.0011
Median of 279 0.0372
Median of 280 0.0169
Median of 281 0.0139
Median of 282 0.0053
Median of 283 2.658
Median of 284 0.0
Median of 285 1.87515
Median of 286 3.36005
Median of 287 0.13895000000000002
Median of 288 0.0036
Median of 289 2.5490000000000004
Median of 290 0.0833
Median of 294 278.6719
Median of 295 195.8256
Median of 296 1202.4121
Median of 297 820.0988
Median of 298 0.0528
Median of 299 0.04
Median of 300 0.0828
Median of 301 0.8604
Median of 302 0.3808
Median of 303 0.0388
Median of 304 0.1372
Median of 305 0.2643
Median of 306 0.0448
Median of 307 0.1295
Median of 308 0.21945
Median of 309 0.1295
Median of 310 0.3029
Median of 311 0.0977
Median of 312 0.1215
Median of 313 0.0
Median of 314 0.0
Median of 315 0.0
Median of 316 5.8315
Median of 317 0.1634
Median of 318 2.8989000000000003
Median of 319 8.3888
Median of 320 0.03985
Median of 321 2.07765
Median of 322 0.0
Median of 323 5.4588
Median of 324 12.5045
Median of 325 0.0
Median of 326 0.0
Median of 327 0.0
Median of 328 0.0
Median of 329 0.0
Median of 330 0.0
Median of 331 0.0848
Median of 332 2.0627000000000004
Median of 333 5.9801
Median of 334 0.1294
Median of 335 2.5135
Median of 336 9.073550000000001
Median of 337 2.05445
Median of 338 2.5608500000000003
Median of 339 9.4742
Median of 340 0.0464
Median of 341 2.3773
Median of 342 0.0
Median of 343 6.0056
Median of 344 23.2147
Median of 347 0.0
Median of 348 0.0226
Median of 349 0.024
Median of 350 0.0188
Median of 351 0.0253
Median of 352 0.022
Median of 353 0.0421
Median of 354 0.0442
Median of 355 0.0294
Median of 356 1.2553
Median of 357 0.0009
Median of 362 0.0125
Median of 363 309.83164999999997
Median of 364 0.0
Median of 365 0.0046
Median of 366 0.0043
Median of 367 0.0032
Median of 368 0.0028
Median of 369 0.0
Median of 370 0.0
Median of 371 0.0
Median of 372 0.0
Median of 373 0.0
Median of 374 0.0
Median of 375 0.0
Median of 376 0.0016
Median of 377 0.0015
Median of 378 0.0
Median of 379 0.0
Median of 380 0.0
Median of 381 0.0
Median of 385 0.0068
Median of 391 0.0102
Median of 394 0.0
Median of 395 0.0
Median of 396 0.0
Median of 397 0.0
Median of 398 0.0
Median of 399 0.0
Median of 400 0.0
Median of 401 0.0
Median of 402 0.0
Median of 403 0.0
Median of 404 0.0
Median of 405 0.0239
Median of 406 5.9201
Median of 407 1.2397
Median of 408 4.9224499999999995
Median of 409 4.4897
Median of 410 4.732749999999999
Median of 411 2.5481
Median of 412 26.1569
Median of 413 20.2551
Median of 414 0.0
Median of 415 6.1766
Median of 416 3.234
Median of 417 7.3956
Median of 418 302.1776
Median of 419 272.4487
Median of 420 1.6451
Median of 421 3.9431
Median of 422 0.0
Median of 423 69.90545
Median of 424 2.6671
Median of 425 4.7644
Median of 426 1.1353
Median of 427 3.94145
Median of 428 2.5341
Median of 430 11.1056
Median of 431 16.381
Median of 432 57.9693
Median of 433 151.1156
Median of 434 10.1977
Median of 435 4.5511
Median of 436 2.7643
Median of 437 3.7809
Median of 438 49.0909
Median of 439 65.4378
Median of 440 12.0859
Median of 441 0.8076
Median of 442 1.2645499999999998
Median of 443 0.6435
Median of 444 0.9027000000000001
Median of 445 0.6511
Median of 446 1.1638
Median of 447 0.2797
Median of 448 0.2512
Median of 449 0.0
Median of 450 0.0
Median of 451 0.0
Median of 452 5.27145
Median of 453 5.2271
Median of 454 7.4249
Median of 455 3.7245
Median of 456 11.3509
Median of 457 4.79335
Median of 458 0.0
Median of 459 2.83035
Median of 460 26.16785
Median of 461 0.0
Median of 462 0.0
Median of 463 0.0
Median of 464 0.0
Median of 465 0.0
Median of 466 0.0
Median of 467 5.645
Median of 468 150.3401
Median of 469 5.4724
Median of 470 4.0611
Median of 471 7.396
Median of 472 138.25515000000001
Median of 473 34.24675
Median of 474 32.820049999999995
Median of 475 4.2762
Median of 476 15.9738
Median of 477 5.2422
Median of 478 0.0
Median of 479 3.1845
Median of 480 70.4345
Median of 481 0.0
Median of 482 293.5185
Median of 483 148.3175
Median of 484 138.7755
Median of 485 112.9534
Median of 486 249.927
Median of 487 112.2755
Median of 488 348.5294
Median of 489 219.4872
Median of 490 48.55745
Median of 491 2.2508
Median of 496 22.0391
Median of 497 10.90655
Median of 498 0.0
Median of 499 0.0
Median of 500 0.0
Median of 501 0.0
Median of 502 0.0
Median of 503 0.0
Median of 504 0.0
Median of 505 0.0
Median of 506 0.0
Median of 507 0.0
Median of 508 0.0
Median of 509 0.0
Median of 510 46.9861
Median of 511 0.0
Median of 512 0.0
Median of 513 0.0
Median of 514 0.0
Median of 515 0.0
Median of 519 5.83295
Median of 525 5.1342
Median of 528 0.0
Median of 529 0.0
Median of 530 0.0
Median of 531 0.0
Median of 532 0.0
Median of 533 0.0
Median of 534 0.0
Median of 535 0.0
Median of 536 0.0
Median of 537 0.0
Median of 538 0.0
Median of 539 3.0548
Median of 540 1.7855
Median of 541 9.4593
Median of 542 0.1096
Median of 543 0.0078
Median of 544 0.0026
Median of 545 7.1160000000000005
Median of 546 0.9111
Median of 547 403.122
Median of 548 74.084
Median of 549 0.471
Median of 550 16.34
Median of 551 1.15
Median of 552 0.1979
Median of 553 7.4279
Median of 554 0.4789
Median of 555 54.4417
Median of 556 4.0671
Median of 557 1.5298
Median of 558 0.9727
Median of 559 0.2909
Median of 560 0.0592
Median of 561 29.73115
Median of 562 264.272
Median of 563 0.651
Median of 564 5.16
Median of 565 0.11954999999999999
Median of 566 2.15045
Median of 567 0.04865
Median of 568 1.9997
Median of 569 16.98835
Median of 582 0.5002
Median of 583 0.0138
Median of 584 0.0036
Median of 585 2.75765
Median of 586 0.0205
Median of 587 0.0148
Median of 588 0.0046
Median of 589 71.9005
ulf.append('Time')
sd1=sd.drop('Time',axis=1)
print('''\n\033[1m''' + '''Checking columns with all zero values:''' + '''\033[0m''')
print('Columns with all zero values are')
total=[]
for x in sd:
if (sd[x] == 0).all() == True:
total.append(x)
print(x)
print('Total columns with all zero rows are',len(total))
Checking columns with all zero values:
Columns with all zero values are
13
52
97
141
149
178
179
186
189
190
191
192
193
194
226
229
230
231
232
233
234
235
236
237
240
241
242
243
256
257
258
259
260
261
262
263
264
265
266
276
284
313
314
315
322
325
326
327
328
329
330
364
369
370
371
372
373
374
375
378
379
380
381
394
395
396
397
398
399
400
401
402
403
404
414
422
449
450
451
458
461
462
463
464
465
466
481
498
501
502
503
504
505
506
507
508
509
512
513
514
515
528
529
530
531
532
533
534
535
536
537
538
Total columns with all zero rows are 112
print('''\n\033[1m''' + '''Droping all columns, with all zero rows''' + '''\033[0m''')
print('''\n\033[1m''' + '''Since having all records as zero means either the sensor is idel or faulty''' + '''\033[0m''')
for x in sd1:
if (sd[x] == 0).all() == True:
ulf.append(x)
sd1=sd1.drop(x,axis=1)
Droping all columns, with all zero rows Since having all records as zero means either the sensor is idel or faulty
print('''\n\033[1m''' + '''Checking for columns with 0 Standard Deviation''' + '''\033[0m''')
stdo = []
for x in sd1:
sd1[x]=sd1[x].astype(float)
att = sd1[x]
std = np.std(att)
if std == 0:
stdo.append(x)
print(stdo)
Checking for columns with 0 Standard Deviation
['5', '42', '49', '69']
co=sd1.corr()
co
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 180 | 181 | 182 | 183 | 184 | 185 | 187 | 188 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 221 | 222 | 223 | 224 | 225 | 227 | 228 | 238 | 239 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 316 | 317 | 318 | 319 | 320 | 321 | 323 | 324 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 359 | 360 | 361 | 362 | 363 | 365 | 366 | 367 | 368 | 376 | 377 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 452 | 453 | 454 | 455 | 456 | 457 | 459 | 460 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 493 | 494 | 495 | 496 | 497 | 499 | 500 | 510 | 511 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 539 | 540 | 541 | 542 | 543 | 544 | 545 | 546 | 547 | 548 | 549 | 550 | 551 | 552 | 553 | 554 | 555 | 556 | 557 | 558 | 559 | 560 | 561 | 562 | 563 | 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.000000 | -0.144161 | 0.004667 | -0.006665 | -0.010819 | NaN | 0.002028 | 0.031347 | -0.052735 | 0.009091 | 0.006445 | 0.043432 | 0.010571 | -0.007191 | 0.030745 | -0.005720 | 0.012496 | 0.017997 | -0.009350 | 0.025773 | -0.011373 | 0.032438 | -0.046631 | -0.015075 | 0.107802 | 0.092542 | 0.106749 | 0.054828 | -0.030918 | -0.039741 | -0.055994 | -0.004630 | -0.034015 | -0.017629 | -0.004498 | 0.017628 | -0.035530 | -0.042294 | -0.034852 | -0.006758 | 0.040578 | NaN | -0.050537 | 0.053885 | 0.015588 | -0.006072 | -0.037488 | 0.004239 | NaN | -0.019961 | -0.050516 | 0.014775 | 0.022436 | -0.025070 | 0.015531 | -0.021465 | -0.012648 | -0.105721 | -0.036916 | 0.033285 | 0.007996 | -0.045733 | -0.017677 | -0.019816 | -0.000472 | -0.023135 | 0.033465 | NaN | -0.001347 | -0.000274 | -0.029430 | 0.082382 | 0.025624 | 0.019914 | 0.071269 | -0.116133 | 0.008173 | 0.039421 | 0.036955 | 0.006073 | 0.053791 | -0.018600 | 0.050358 | -0.029585 | -0.012823 | 0.012190 | 0.014710 | -0.008695 | 0.003104 | -0.006371 | -0.040050 | 0.015871 | -0.032758 | -0.059082 | 0.030687 | -0.023573 | -0.048171 | -0.072362 | 0.055114 | 0.010636 | -0.007893 | -0.004425 | -0.017498 | -0.023871 | 0.018098 | -0.007362 | 0.023273 | 0.056929 | -0.000719 | -0.013161 | -0.011252 | 0.004043 | -0.010883 | 0.000068 | -0.019901 | -0.029252 | 0.032205 | -0.025119 | -0.008852 | -0.050394 | -0.069598 | 0.012123 | 0.025637 | 0.031306 | -0.007899 | 0.012053 | -0.070741 | 0.157779 | 0.160622 | -0.021229 | 0.002820 | -0.010912 | -0.007089 | -0.001182 | 0.024052 | -0.024102 | -0.031529 | -0.017700 | 0.000572 | 0.006897 | -0.014043 | 0.003557 | -0.030330 | -0.009369 | 0.005503 | 0.041362 | -0.036601 | -0.003436 | 0.014701 | 0.064169 | -0.043381 | -0.056778 | -0.054278 | 0.002513 | 0.029730 | 0.015470 | 0.049901 | -0.006522 | -0.024110 | 0.006391 | 0.036074 | 0.006345 | 0.027508 | -0.013232 | -0.043054 | -0.002019 | 0.001997 | 0.030245 | -0.023445 | 0.005133 | -0.006843 | -0.012965 | -0.046820 | 0.020889 | 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0.002978 | 0.032026 | -0.013451 | 0.003620 | -0.006981 | -0.005441 | -0.037936 | 0.024618 | -0.022746 | -0.016539 | -0.028966 | -0.017602 | -0.023392 | -0.004593 | -0.022441 | -0.024550 | -0.023296 | -0.031526 | -0.029430 | -0.025635 | -0.036949 | -0.029430 | -0.014696 | -0.026618 | -0.030720 | -0.030673 | 0.022020 | -0.012290 | 0.064804 | -0.027627 | -0.023292 | -0.075711 | -0.006287 | -0.023994 | -0.022560 | -0.012885 | -0.027926 | -0.023784 | -0.022711 | -0.009785 | -0.011074 | -0.027474 | -0.001904 | -0.040219 | -0.016010 | -0.010756 | -0.000335 | 0.000803 | -0.004335 | -0.006066 | 0.002155 | -0.082079 | 0.078467 | -0.026281 | 0.035716 | -0.071909 | 0.091673 | 0.171360 | -0.022126 | 0.008603 | -0.022163 | -0.007519 | -0.000818 | 0.030612 | -0.001992 | 0.016545 | -0.015883 | -0.001002 | 0.027067 | -0.029763 | -0.007086 | -0.028482 | -0.008274 | 0.005252 | 0.039126 | -0.055868 | -0.041032 | -0.010235 | 0.063043 | -0.064100 | -0.065129 | -0.064498 | -0.005586 | 0.026847 | 0.032465 | 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0.060397 | 0.044608 | 0.063064 | 0.052853 | -0.018987 | -0.023194 | 0.013715 | -0.002035 | 0.015242 | 0.007014 | 0.013265 | 0.008639 | 0.000190 | 0.023562 | 0.019994 | 0.023695 | 0.018534 | -0.025867 | -0.028161 | 0.004185 | -0.025100 |
| 1 | -0.144161 | 1.000000 | 0.005883 | -0.008963 | -0.001917 | NaN | -0.025222 | -0.011761 | 0.031244 | 0.024025 | 0.009529 | -0.027042 | 0.034308 | -0.037730 | -0.087226 | -0.001812 | -0.010076 | 0.043390 | -0.003172 | 0.032532 | 0.058213 | -0.052712 | -0.015985 | -0.059887 | 0.004832 | -0.023953 | 0.003840 | -0.022899 | 0.001764 | 0.052170 | -0.051542 | -0.044621 | -0.021594 | -0.060610 | -0.065810 | 0.060611 | -0.008617 | 0.008524 | -0.055925 | 0.028232 | -0.017729 | NaN | -0.020209 | 0.005930 | 0.017978 | 0.008007 | 0.019765 | 0.005557 | NaN | 0.004848 | 0.023216 | -0.034694 | -0.046867 | 0.016996 | 0.001139 | 0.037361 | -0.025777 | 0.007096 | -0.035086 | 0.014362 | -0.013686 | 0.038388 | 0.058734 | 0.054195 | -0.019909 | 0.002603 | 0.018910 | NaN | -0.026031 | -0.018365 | 0.002818 | -0.051337 | -0.045856 | -0.013624 | 0.005890 | 0.006318 | 0.004938 | 0.014081 | 0.010056 | -0.003959 | 0.058009 | 0.019425 | -0.036489 | -0.007163 | 0.015344 | 0.019586 | 0.022415 | -0.013594 | 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| 2 | 0.004667 | 0.005883 | 1.000000 | 0.298810 | 0.095881 | NaN | -0.136212 | -0.147116 | 0.023524 | 0.016248 | 0.069902 | 0.036368 | 0.018359 | 0.006494 | 0.006116 | -0.000805 | -0.004898 | 0.021890 | -0.026671 | 0.015437 | 0.044653 | -0.029961 | 0.009196 | -0.021048 | -0.025910 | -0.027251 | -0.025128 | -0.061773 | 0.049783 | 0.062159 | -0.007950 | -0.023447 | -0.043049 | 0.003867 | 0.015026 | -0.003868 | -0.003267 | -0.009020 | -0.004802 | -0.012922 | -0.015081 | NaN | 0.050638 | -0.091982 | 0.005947 | 0.010267 | -0.041489 | -0.096099 | NaN | -0.008427 | -0.016603 | -0.001070 | 0.015645 | -0.007971 | -0.018766 | -0.046616 | 0.014790 | 0.039275 | 0.038660 | -0.032510 | -0.013188 | -0.028929 | 0.007370 | 0.013832 | 0.001638 | -0.013379 | -0.022808 | NaN | 0.003058 | 0.053691 | 0.030593 | -0.058994 | -0.007482 | -0.023158 | 0.001248 | 0.065188 | 0.020728 | 0.004893 | 0.013222 | -0.006109 | -0.013232 | -0.009287 | -0.023048 | 0.000565 | 0.026593 | 0.040486 | -0.015209 | -0.023641 | 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| -0.009022 | 0.008358 | -0.031762 | 0.042449 | -0.022357 | 0.033403 | -0.017837 | -0.046160 | -0.000343 | 0.022099 | -0.020136 | 0.013407 | -0.037958 | -0.073672 | -0.017111 | 0.004075 | -0.035332 | -0.007164 | -0.033692 | -0.052934 | -0.007686 | -0.046518 | -0.023453 | 0.030593 | -0.008611 | -0.046087 | -0.024018 | -0.032143 | -0.009122 | 0.012599 | -0.004110 | 0.008355 | 0.021481 | -0.023568 | 0.003086 | 0.015112 | 0.018699 | -0.007186 | 0.014197 | 0.051230 | 0.044805 | 0.002747 | -0.007480 | 0.016503 | 0.062135 | 0.041838 | -0.023748 | -0.020406 | 0.013495 | -0.029515 | 0.014928 | -0.005773 | -0.049960 | -0.016598 | 0.042220 | -0.085836 | -0.004150 | 0.012451 | 0.025961 | 0.034083 | 0.025285 | -0.025515 | 0.030781 | -0.011749 | -0.003707 | 0.021361 | 0.016429 | -0.002546 | 0.017842 | 0.013257 | -0.001327 | 0.018934 | 0.017993 | 0.030026 | 0.028568 | 0.026989 | 0.025934 | 0.014280 | -0.021279 | 0.047050 | 0.042052 | 0.047528 | 0.037165 | 0.047405 | 0.061432 | -0.037054 | -0.015672 | 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| 3 | -0.006665 | -0.008963 | 0.298810 | 1.000000 | -0.058351 | NaN | -0.685773 | 0.086965 | -0.102839 | 0.066987 | 0.049785 | 0.039943 | -0.028949 | -0.020144 | -0.013440 | -0.004475 | 0.042937 | -0.029514 | 0.040384 | 0.037980 | 0.020765 | -0.033073 | -0.006984 | -0.049871 | 0.078607 | 0.035440 | 0.084145 | 0.096031 | -0.013284 | 0.001784 | -0.107694 | -0.020255 | -0.040988 | -0.005983 | 0.056176 | 0.005984 | 0.000569 | -0.052534 | 0.020904 | -0.005794 | -0.020068 | NaN | -0.023118 | 0.064861 | -0.032105 | -0.043843 | -0.000062 | -0.022463 | NaN | -0.057645 | -0.012708 | -0.049702 | -0.047006 | -0.016276 | -0.028253 | -0.004177 | 0.033475 | -0.020819 | -0.026427 | 0.040335 | -0.009078 | -0.032268 | -0.026777 | -0.028620 | -0.028809 | 0.009529 | 0.017579 | NaN | -0.016733 | 0.081688 | 0.008690 | -0.028246 | 0.068534 | -0.007956 | 0.126423 | -0.017482 | 0.087900 | 0.048279 | 0.011926 | 0.065560 | 0.018970 | -0.023327 | -0.003854 | -0.017118 | 0.006927 | -0.008433 | 0.042842 | -0.068630 | 0.027857 | -0.019937 | -0.026869 | 0.033634 | -0.037255 | 0.010948 | 0.006993 | -0.054940 | -0.038321 | -0.088792 | -0.014000 | 0.066469 | -0.025658 | 0.061013 | -0.009732 | 0.011730 | 0.029473 | -0.022967 | -0.003947 | -0.038273 | 0.037826 | -0.001377 | 0.007754 | -0.012487 | -0.093261 | -0.005958 | -0.089669 | -0.157126 | 0.111304 | 0.019612 | -0.005420 | 0.027219 | -0.209380 | -0.013580 | 0.007591 | 0.027091 | -0.000082 | 0.042486 | 0.051557 | 0.021296 | -0.006029 | -0.205748 | 0.300190 | -0.059587 | -0.073512 | -0.247119 | 0.013332 | -0.077742 | -0.029134 | -0.013715 | 0.008714 | -0.046007 | -0.007516 | 0.007971 | -0.049124 | 0.012886 | -0.036567 | -0.001706 | -0.050424 | -0.075866 | -0.009108 | 0.041694 | -0.047484 | -0.045561 | -0.056278 | 0.035605 | 0.024249 | 0.024307 | 0.060920 | -0.004868 | 0.021809 | 0.004056 | 0.006107 | 0.004028 | 0.050747 | -0.037996 | 0.008217 | 0.071771 | 0.040891 | 0.021622 | -0.022123 | -0.028755 | -0.007808 | 0.002899 | -0.030464 | -0.004628 | 0.000329 | 0.009062 | 0.027639 | 0.015879 | 0.007865 | -0.005706 | 0.014156 | 0.004440 | 0.012997 | -0.004293 | 0.008690 | 0.016498 | -0.019346 | 0.008690 | -0.036448 | 0.036122 | -0.028252 | -0.053151 | -0.040885 | -0.077789 | 0.035694 | -0.038834 | -0.034031 | -0.059125 | 0.012829 | -0.023927 | 0.013004 | -0.003845 | -0.033906 | -0.084816 | -0.027137 | -0.045036 | 0.001854 | -0.024229 | -0.032694 | -0.028031 | -0.039871 | 0.032382 | 0.039710 | -0.007980 | -0.016953 | -0.092608 | 0.172735 | -0.132051 | 0.030149 | 0.055338 | 0.009355 | -0.010101 | -0.328922 | 0.308207 | -0.059598 | -0.086566 | -0.255999 | 0.008918 | -0.067874 | -0.027022 | -0.014649 | 0.007996 | -0.033921 | -0.006519 | 0.007948 | -0.048350 | 0.010639 | -0.046415 | 0.005056 | -0.052479 | -0.074062 | -0.012688 | 0.041959 | -0.047408 | -0.046424 | -0.057036 | 0.053077 | 0.025677 | 0.022358 | 0.064156 | -0.005158 | 0.025959 | 0.001070 | 0.005428 | 0.001108 | 0.051992 | -0.038533 | 0.006341 | 0.049431 | 0.036496 | 0.033285 | -0.012596 | -0.024176 | -0.005746 | 0.003529 | -0.027472 | -0.004353 | 0.006048 | 0.011974 | 0.018339 | 0.019338 | 0.011590 | -0.008776 | 0.014205 | 0.013095 | 0.012027 | 0.000245 | 0.008690 | 0.023493 | -0.021182 | 0.008690 | -0.058285 | 0.031793 | -0.024551 | -0.049738 | -0.029522 | -0.095838 | 0.039312 | -0.041228 | -0.026867 | -0.056371 | 0.023155 | -0.016681 | 0.009384 | -0.002131 | -0.038977 | -0.084497 | -0.036636 | -0.055528 | -0.030245 | -0.051620 | -0.008852 | -0.032320 | -0.032045 | -0.027574 | -0.047861 | 0.032513 | 0.039619 | -0.002405 | -0.019154 | -0.101053 | 0.175168 | -0.146788 | 0.014310 | 0.053905 | 0.021850 | -0.005102 | -0.229881 | -0.182281 | 0.010070 | 0.005375 | -0.257674 | 0.024222 | -0.003374 | 0.034009 | -0.012723 | 0.009131 | -0.017739 | -0.008348 | 0.009146 | -0.049770 | 0.012588 | -0.034611 | -0.003708 | -0.043265 | -0.056260 | 0.054295 | 0.018093 | -0.041120 | -0.035356 | -0.043838 | 0.020848 | -0.008501 | 0.020946 | 0.069024 | -0.002670 | 0.031215 | 0.004133 | 0.001950 | 0.003751 | 0.050329 | -0.037190 | 0.007421 | 0.073772 | 0.037188 | 0.025580 | -0.019373 | -0.025981 | -0.007307 | 0.004689 | -0.016962 | -0.005958 | 0.014598 | 0.010490 | 0.026457 | 0.017322 | 0.055020 | 0.000922 | 0.027859 | 0.005244 | -0.024735 | -0.003068 | 0.008690 | 0.016655 | -0.071271 | 0.029765 | 0.023176 | -0.057754 | -0.013765 | -0.000351 | 0.061805 | 0.009301 | -0.032383 | -0.044107 | -0.060643 | 0.013617 | -0.019551 | 0.011487 | 0.003101 | -0.033454 | 0.007274 | 0.016881 | -0.079421 | -0.021434 | -0.027982 | -0.032547 | -0.019704 | -0.040345 | 0.031900 | 0.040870 | -0.008860 | -0.017570 | -0.092774 | 0.173252 | -0.131853 | 0.024671 | 0.154507 | 0.065219 | -0.054575 | 0.045204 | -0.068201 | -0.049059 | -0.055737 | -0.047487 | -0.018437 | -0.019657 | -0.045494 | -0.025577 | -0.023407 | -0.057669 | -0.017262 | -0.016666 | 0.083049 | 0.025931 | 0.013824 | 0.014983 | 0.035123 | -0.015811 | 0.028468 | 0.041155 | 0.027368 | 0.044403 | 0.024604 | 0.060694 | 0.002004 | -0.016797 | 0.008152 | 0.013742 | 0.007345 | 0.007445 | 0.008741 | 0.016888 | 0.008305 | 0.025288 | 0.024615 | 0.025882 | 0.035164 | -0.039196 | -0.033755 | -0.080942 | -0.024014 |
| 4 | -0.010819 | -0.001917 | 0.095881 | -0.058351 | 1.000000 | NaN | -0.074395 | -0.343413 | -0.025933 | 0.053973 | -0.006496 | -0.001137 | -0.002800 | -0.017583 | 0.011395 | -0.001730 | 0.000312 | -0.001742 | 0.007237 | 0.012824 | 0.008305 | -0.007777 | 0.005168 | -0.013682 | 0.028113 | 0.010989 | 0.028485 | 0.007573 | -0.012846 | 0.001958 | -0.035120 | -0.005429 | 0.012242 | 0.002176 | -0.006118 | -0.002176 | 0.005419 | -0.015762 | 0.011830 | 0.025438 | -0.012559 | NaN | -0.012049 | -0.009200 | 0.057784 | 0.047077 | 0.003056 | -0.028205 | NaN | 0.029014 | 0.006754 | 0.031231 | 0.029881 | -0.052418 | -0.033162 | 0.001961 | -0.023649 | -0.020492 | -0.008343 | -0.031664 | 0.049108 | -0.004528 | 0.002886 | 0.006118 | 0.045045 | -0.002915 | -0.016009 | NaN | 0.031992 | 0.037035 | -0.000965 | 0.023089 | 0.042741 | -0.000693 | 0.051036 | -0.056407 | -0.001538 | 0.007521 | -0.014557 | -0.004652 | -0.006830 | 0.009175 | 0.010103 | -0.007154 | -0.012134 | -0.013849 | -0.020368 | 0.011625 | 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451 rows × 451 columns
upper=co.where(np.triu(np.ones(co.shape),k=1).astype(np.bool))
upper
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 180 | 181 | 182 | 183 | 184 | 185 | 187 | 188 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 221 | 222 | 223 | 224 | 225 | 227 | 228 | 238 | 239 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 316 | 317 | 318 | 319 | 320 | 321 | 323 | 324 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 359 | 360 | 361 | 362 | 363 | 365 | 366 | 367 | 368 | 376 | 377 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 452 | 453 | 454 | 455 | 456 | 457 | 459 | 460 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 493 | 494 | 495 | 496 | 497 | 499 | 500 | 510 | 511 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 539 | 540 | 541 | 542 | 543 | 544 | 545 | 546 | 547 | 548 | 549 | 550 | 551 | 552 | 553 | 554 | 555 | 556 | 557 | 558 | 559 | 560 | 561 | 562 | 563 | 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| 1 | NaN | NaN | 0.005883 | -0.008963 | -0.001917 | NaN | -0.025222 | -0.011761 | 0.031244 | 0.024025 | 0.009529 | -0.027042 | 0.034308 | -0.037730 | -0.087226 | -0.001812 | -0.010076 | 0.043390 | -0.003172 | 0.032532 | 0.058213 | -0.052712 | -0.015985 | -0.059887 | 0.004832 | -0.023953 | 0.003840 | -0.022899 | 0.001764 | 0.052170 | -0.051542 | -0.044621 | -0.021594 | -0.060610 | -0.065810 | 0.060611 | -0.008617 | 0.008524 | -0.055925 | 0.028232 | -0.017729 | NaN | -0.020209 | 0.005930 | 0.017978 | 0.008007 | 0.019765 | 0.005557 | NaN | 0.004848 | 0.023216 | -0.034694 | -0.046867 | 0.016996 | 0.001139 | 0.037361 | -0.025777 | 0.007096 | -0.035086 | 0.014362 | -0.013686 | 0.038388 | 0.058734 | 0.054195 | -0.019909 | 0.002603 | 0.018910 | NaN | -0.026031 | -0.018365 | 0.002818 | -0.051337 | -0.045856 | -0.013624 | 0.005890 | 0.006318 | 0.004938 | 0.014081 | 0.010056 | -0.003959 | 0.058009 | 0.019425 | -0.036489 | -0.007163 | 0.015344 | 0.019586 | 0.022415 | -0.013594 | -0.022434 | 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| 2 | NaN | NaN | NaN | 0.298810 | 0.095881 | NaN | -0.136212 | -0.147116 | 0.023524 | 0.016248 | 0.069902 | 0.036368 | 0.018359 | 0.006494 | 0.006116 | -0.000805 | -0.004898 | 0.021890 | -0.026671 | 0.015437 | 0.044653 | -0.029961 | 0.009196 | -0.021048 | -0.025910 | -0.027251 | -0.025128 | -0.061773 | 0.049783 | 0.062159 | -0.007950 | -0.023447 | -0.043049 | 0.003867 | 0.015026 | -0.003868 | -0.003267 | -0.009020 | -0.004802 | -0.012922 | -0.015081 | NaN | 0.050638 | -0.091982 | 0.005947 | 0.010267 | -0.041489 | -0.096099 | NaN | -0.008427 | -0.016603 | -0.001070 | 0.015645 | -0.007971 | -0.018766 | -0.046616 | 0.014790 | 0.039275 | 0.038660 | -0.032510 | -0.013188 | -0.028929 | 0.007370 | 0.013832 | 0.001638 | -0.013379 | -0.022808 | NaN | 0.003058 | 0.053691 | 0.030593 | -0.058994 | -0.007482 | -0.023158 | 0.001248 | 0.065188 | 0.020728 | 0.004893 | 0.013222 | -0.006109 | -0.013232 | -0.009287 | -0.023048 | 0.000565 | 0.026593 | 0.040486 | -0.015209 | -0.023641 | 0.016083 | 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| 3 | NaN | NaN | NaN | NaN | -0.058351 | NaN | -0.685773 | 0.086965 | -0.102839 | 0.066987 | 0.049785 | 0.039943 | -0.028949 | -0.020144 | -0.013440 | -0.004475 | 0.042937 | -0.029514 | 0.040384 | 0.037980 | 0.020765 | -0.033073 | -0.006984 | -0.049871 | 0.078607 | 0.035440 | 0.084145 | 0.096031 | -0.013284 | 0.001784 | -0.107694 | -0.020255 | -0.040988 | -0.005983 | 0.056176 | 0.005984 | 0.000569 | -0.052534 | 0.020904 | -0.005794 | -0.020068 | NaN | -0.023118 | 0.064861 | -0.032105 | -0.043843 | -0.000062 | -0.022463 | NaN | -0.057645 | -0.012708 | -0.049702 | -0.047006 | -0.016276 | -0.028253 | -0.004177 | 0.033475 | -0.020819 | -0.026427 | 0.040335 | -0.009078 | -0.032268 | -0.026777 | -0.028620 | -0.028809 | 0.009529 | 0.017579 | NaN | -0.016733 | 0.081688 | 0.008690 | -0.028246 | 0.068534 | -0.007956 | 0.126423 | -0.017482 | 0.087900 | 0.048279 | 0.011926 | 0.065560 | 0.018970 | -0.023327 | -0.003854 | -0.017118 | 0.006927 | -0.008433 | 0.042842 | -0.068630 | 0.027857 | 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0.009062 | 0.027639 | 0.015879 | 0.007865 | -0.005706 | 0.014156 | 0.004440 | 0.012997 | -0.004293 | 0.008690 | 0.016498 | -0.019346 | 0.008690 | -0.036448 | 0.036122 | -0.028252 | -0.053151 | -0.040885 | -0.077789 | 0.035694 | -0.038834 | -0.034031 | -0.059125 | 0.012829 | -0.023927 | 0.013004 | -0.003845 | -0.033906 | -0.084816 | -0.027137 | -0.045036 | 0.001854 | -0.024229 | -0.032694 | -0.028031 | -0.039871 | 0.032382 | 0.039710 | -0.007980 | -0.016953 | -0.092608 | 0.172735 | -0.132051 | 0.030149 | 0.055338 | 0.009355 | -0.010101 | -0.328922 | 0.308207 | -0.059598 | -0.086566 | -0.255999 | 0.008918 | -0.067874 | -0.027022 | -0.014649 | 0.007996 | -0.033921 | -0.006519 | 0.007948 | -0.048350 | 0.010639 | -0.046415 | 0.005056 | -0.052479 | -0.074062 | -0.012688 | 0.041959 | -0.047408 | -0.046424 | -0.057036 | 0.053077 | 0.025677 | 0.022358 | 0.064156 | -0.005158 | 0.025959 | 0.001070 | 0.005428 | 0.001108 | 0.051992 | -0.038533 | 0.006341 | 0.049431 | 0.036496 | 0.033285 | -0.012596 | -0.024176 | -0.005746 | 0.003529 | -0.027472 | -0.004353 | 0.006048 | 0.011974 | 0.018339 | 0.019338 | 0.011590 | -0.008776 | 0.014205 | 0.013095 | 0.012027 | 0.000245 | 0.008690 | 0.023493 | -0.021182 | 0.008690 | -0.058285 | 0.031793 | -0.024551 | -0.049738 | -0.029522 | -0.095838 | 0.039312 | -0.041228 | -0.026867 | -0.056371 | 0.023155 | -0.016681 | 0.009384 | -0.002131 | -0.038977 | -0.084497 | -0.036636 | -0.055528 | -0.030245 | -0.051620 | -0.008852 | -0.032320 | -0.032045 | -0.027574 | -0.047861 | 0.032513 | 0.039619 | -0.002405 | -0.019154 | -0.101053 | 0.175168 | -0.146788 | 0.014310 | 0.053905 | 0.021850 | -0.005102 | -0.229881 | -0.182281 | 0.010070 | 0.005375 | -0.257674 | 0.024222 | -0.003374 | 0.034009 | -0.012723 | 0.009131 | -0.017739 | -0.008348 | 0.009146 | -0.049770 | 0.012588 | -0.034611 | -0.003708 | -0.043265 | -0.056260 | 0.054295 | 0.018093 | -0.041120 | -0.035356 | -0.043838 | 0.020848 | -0.008501 | 0.020946 | 0.069024 | -0.002670 | 0.031215 | 0.004133 | 0.001950 | 0.003751 | 0.050329 | -0.037190 | 0.007421 | 0.073772 | 0.037188 | 0.025580 | -0.019373 | -0.025981 | -0.007307 | 0.004689 | -0.016962 | -0.005958 | 0.014598 | 0.010490 | 0.026457 | 0.017322 | 0.055020 | 0.000922 | 0.027859 | 0.005244 | -0.024735 | -0.003068 | 0.008690 | 0.016655 | -0.071271 | 0.029765 | 0.023176 | -0.057754 | -0.013765 | -0.000351 | 0.061805 | 0.009301 | -0.032383 | -0.044107 | -0.060643 | 0.013617 | -0.019551 | 0.011487 | 0.003101 | -0.033454 | 0.007274 | 0.016881 | -0.079421 | -0.021434 | -0.027982 | -0.032547 | -0.019704 | -0.040345 | 0.031900 | 0.040870 | -0.008860 | -0.017570 | -0.092774 | 0.173252 | -0.131853 | 0.024671 | 0.154507 | 0.065219 | -0.054575 | 0.045204 | -0.068201 | -0.049059 | -0.055737 | -0.047487 | -0.018437 | -0.019657 | -0.045494 | -0.025577 | -0.023407 | -0.057669 | -0.017262 | -0.016666 | 0.083049 | 0.025931 | 0.013824 | 0.014983 | 0.035123 | -0.015811 | 0.028468 | 0.041155 | 0.027368 | 0.044403 | 0.024604 | 0.060694 | 0.002004 | 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| 4 | NaN | NaN | NaN | NaN | NaN | NaN | -0.074395 | -0.343413 | -0.025933 | 0.053973 | -0.006496 | -0.001137 | -0.002800 | -0.017583 | 0.011395 | -0.001730 | 0.000312 | -0.001742 | 0.007237 | 0.012824 | 0.008305 | -0.007777 | 0.005168 | -0.013682 | 0.028113 | 0.010989 | 0.028485 | 0.007573 | -0.012846 | 0.001958 | -0.035120 | -0.005429 | 0.012242 | 0.002176 | -0.006118 | -0.002176 | 0.005419 | -0.015762 | 0.011830 | 0.025438 | -0.012559 | NaN | -0.012049 | -0.009200 | 0.057784 | 0.047077 | 0.003056 | -0.028205 | NaN | 0.029014 | 0.006754 | 0.031231 | 0.029881 | -0.052418 | -0.033162 | 0.001961 | -0.023649 | -0.020492 | -0.008343 | -0.031664 | 0.049108 | -0.004528 | 0.002886 | 0.006118 | 0.045045 | -0.002915 | -0.016009 | NaN | 0.031992 | 0.037035 | -0.000965 | 0.023089 | 0.042741 | -0.000693 | 0.051036 | -0.056407 | -0.001538 | 0.007521 | -0.014557 | -0.004652 | -0.006830 | 0.009175 | 0.010103 | -0.007154 | -0.012134 | -0.013849 | -0.020368 | 0.011625 | 0.014467 | 0.044905 | 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| -0.014852 | 0.008676 | -0.032573 | -0.017219 | -0.001045 | 0.003581 | -0.021221 | -0.016735 | -0.003062 | 0.010895 | -0.001066 | 0.010119 | -0.000078 | 0.026276 | -0.005804 | 0.002952 | -0.000629 | -0.004856 | 0.028315 | 0.014887 | -0.001401 | -0.015727 | -0.005210 | -0.000965 | 0.004143 | -0.024413 | -0.031016 | -0.027349 | -0.012957 | 0.017688 | 0.021979 | 0.010845 | 0.021393 | 0.020679 | -0.001710 | 0.014760 | 0.015262 | -0.005002 | -0.008887 | 0.002985 | 0.014607 | 0.004512 | 0.004514 | -0.002783 | -0.005128 | -0.013643 | -0.003473 | -0.005579 | 0.008902 | 0.007530 | 0.001463 | -0.012031 | 0.005419 | -0.028575 | 0.059674 | -0.011722 | -0.044401 | 0.032654 | 0.058445 | 0.027900 | 0.056890 | -0.004234 | -0.010649 | -0.041036 | -0.014101 | 0.006564 | -0.012018 | -0.012639 | -0.002593 | -0.013138 | -0.021112 | 0.006580 | -0.010158 | -0.024413 | -0.027078 | -0.028793 | -0.025772 | 0.039755 | 0.034719 | -0.006128 | 0.043493 | -0.010745 | 0.039843 | -0.008487 | 0.031196 | 0.005227 | -0.081776 | -0.011918 | -0.016415 | -0.012158 | -0.017775 | -0.012056 | -0.003978 | 0.044943 | -0.001331 | -0.001621 | -0.001645 | -0.043831 | -0.031012 | -0.026115 | 0.050792 | -0.013633 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 586 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.167920 | 0.164247 | -0.486530 | 0.004170 |
| 587 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.974276 | 0.390834 | 0.035423 |
| 588 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.389238 | 0.031207 |
| 589 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -0.002603 |
| Pass/Fail | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
451 rows × 451 columns
l=[column for column in upper.columns if any (upper[column]>.8)]
for x in l:
ulf.append(x)
sd1=sd1.drop(x,axis=1)
n=1
pds2=sd[['5','42','49','69']]
for x in pds2:
series = pds2[x]
skewness = series.skew()
if skewness > -.5 and skewness < .5 :
print(x,"is Symmetrically Skewed as Skewness =",round(skewness,3),'\n')
elif skewness > .25:
print(x,"is Positively Skewed towards Right side of asymmetric distribution as Skewness =",round(skewness,3),'\n')
elif skewness < -.25:
print(x,"is Negatively Skewed towards Left side of asymmetric distribution as Skewness =",round(skewness,3),'\n')
plt.figure(figsize= (5,5))
plt.subplot(2,2,n)
n=n+1
sns.distplot(pds2[x])
plt.xlabel(x)
plt.show()
5 is Symmetrically Skewed as Skewness = 0
42 is Symmetrically Skewed as Skewness = 0
49 is Symmetrically Skewed as Skewness = 0
69 is Symmetrically Skewed as Skewness = 0
print('''\n\033[1m''' + '''Droping all columns, of NaN correlaton with Pass/Fail column''' + '''\033[0m''')
stdo = []
for x in sd1:
sd1[x]=sd1[x].astype(float)
att = sd1[x]
std = np.std(att)
if std == 0:
stdo.append(x)
ulf.append(x)
sd1=sd1.drop(x,axis=1)
print('Which are:',stdo)
Droping all columns, of NaN correlaton with Pass/Fail column
Which are: ['5', '42', '49', '69']
print('''\n\033[1m''' + '''Checking columns having low variance''' + '''\033[0m''')
from sklearn.feature_selection import VarianceThreshold
selector = VarianceThreshold(threshold=(0.01))
selector.fit(sd1)
Checking columns having low variance
VarianceThreshold(threshold=0.01)
cols=[column for column in sd1.columns
if column not in sd1.columns[selector.get_support()]]
print('''\n\033[1m''' + '''''' + '''\033[0m''',)
print('Total columns having variance less than .01 are:\n',len(cols),'\nWhich are\n',list(cols))
Total columns having variance less than .01 are: 91 Which are ['7', '8', '9', '10', '11', '17', '20', '53', '56', '57', '58', '75', '76', '77', '78', '79', '80', '81', '82', '84', '86', '87', '89', '91', '92', '93', '94', '95', '99', '100', '102', '103', '104', '105', '106', '107', '108', '112', '113', '114', '116', '118', '119', '121', '130', '131', '132', '143', '144', '145', '146', '153', '156', '168', '171', '172', '173', '176', '210', '211', '212', '213', '214', '215', '216', '217', '219', '221', '222', '224', '227', '228', '238', '239', '247', '248', '251', '253', '254', '267', '367', '368', '542', '543', '544', '558', '565', '582', '583', '586', '587']
for x in cols:
ulf.append(x)
sd1=sd1.drop(cols,axis=1)
sd1
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | 18 | 19 | 21 | 22 | 23 | 24 | 25 | 28 | 29 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 43 | 44 | 45 | 47 | 48 | 51 | 55 | 59 | 61 | 62 | 63 | 64 | 67 | 68 | 71 | 74 | 83 | 88 | 90 | 96 | 115 | 117 | 120 | 122 | 125 | 126 | 128 | 129 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 142 | 150 | 151 | 155 | 159 | 160 | 161 | 162 | 163 | 166 | 167 | 169 | 170 | 175 | 177 | 180 | 181 | 182 | 183 | 184 | 185 | 188 | 195 | 198 | 200 | 201 | 208 | 218 | 223 | 225 | 250 | 255 | 268 | 269 | 418 | 419 | 423 | 432 | 433 | 438 | 439 | 460 | 468 | 472 | 474 | 476 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 499 | 500 | 510 | 511 | 521 | 546 | 547 | 548 | 549 | 550 | 551 | 559 | 562 | 563 | 564 | 570 | 571 | 572 | 573 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3030.93 | 2564.00 | 2187.7333 | 1411.1265 | 1.3602 | 97.6133 | 202.4396 | 7.9558 | 414.8710 | 10.04330 | 192.3963 | 12.5190 | -5419.00 | 2916.50 | -4043.75 | 751.00 | 0.8955 | 64.2333 | 2.0222 | 3.5191 | 83.3971 | 9.5126 | 50.6170 | 64.2588 | 49.3830 | 66.3141 | 86.9555 | 117.5132 | 61.29 | 4.515 | 352.7173 | 10.1841 | 130.3691 | 1.3072 | 141.2282 | 218.3174 | 2834.0 | -1.7264 | 10.6231 | 108.6427 | 16.1445 | 21.7264 | 0.9226 | 148.6009 | 84.0793 | 0.0 | 7.2163 | 1747.6049 | 8671.9301 | -0.2786 | 748.6115 | 58.4306 | 6.3788 | 2.639 | 0.8656 | 3.353 | 3.188 | -0.0473 | 1000.7263 | 39.2373 | 123.0 | 111.3 | 75.2 | 46.2000 | 350.6710 | 6.78 | 4.271 | 10.2840 | 0.41 | 1017.0 | 967.0 | 1066.0 | 368.0 | 0.090 | 2.0 | 0.9 | 0.046 | 0.7250 | 0.9499 | 0.160 | 20.95 | 0.333 | 12.49 | 16.713 | 0.0803 | 5.72 | 65.363 | 0.292 | 0.296 | 10.30 | 5.38 | 97.314 | 3.4789 | 175.2173 | 1940.3994 | 219.9453 | 0.4269 | 40.855 | 4.5152 | 525.0965 | 0.0000 | 53.68400 | 26.3617 | 49.0013 | 44.5055 | 42.2737 | 29.9394 | 311.6377 | 63.7987 | 13.6778 | 31.9893 | 613.3069 | 291.4842 | 494.6996 | 178.1759 | 843.1138 | 0.0000 | 53.1098 | 0.0000 | 0.0000 | 0.0000 | 64.6707 | 0.0000 | 0.0 | 1.0616 | 395.570 | 75.752 | 0.4234 | 12.93 | 0.78 | 0.4385 | 264.272 | 0.6510 | 5.16 | 533.8500 | 2.1113 | 8.95 | 0.3157 | 71.9005 | -1.0 |
| 1 | 3095.78 | 2465.14 | 2230.4222 | 1463.6606 | 0.8294 | 102.3433 | 200.5470 | 10.1548 | 414.7347 | 9.25990 | 191.2872 | 12.4608 | -5441.50 | 2604.25 | -3498.75 | -1640.25 | 1.2973 | 68.4222 | 2.2667 | 3.4171 | 84.9052 | 9.7997 | 50.6596 | 64.2828 | 49.3404 | 64.9193 | 87.5241 | 118.1188 | 78.25 | 2.773 | 352.2445 | 10.0373 | 133.1727 | 1.2887 | 145.8445 | 205.1695 | 2853.0 | 0.8073 | 10.3092 | 113.9800 | 10.9036 | 19.1927 | 1.1598 | 154.3709 | 82.3494 | 0.0 | 6.8043 | 1931.6464 | 8407.0299 | 0.5854 | 731.2517 | 58.6680 | 6.5061 | 2.541 | 0.8703 | 2.771 | 3.272 | -0.0946 | 998.1081 | 37.9213 | 98.0 | 80.3 | 81.0 | 56.2000 | 219.7679 | 5.70 | 6.285 | 13.0770 | 0.35 | 568.0 | 59.0 | 297.0 | 3277.0 | 0.112 | 2.2 | 1.1 | 0.561 | 1.0498 | 1.0181 | 0.325 | 17.99 | 0.439 | 10.14 | 16.358 | 0.0892 | 6.92 | 82.986 | 0.222 | 0.316 | 8.02 | 3.74 | 134.250 | 3.9578 | 128.4285 | 1988.0000 | 193.0287 | 0.5749 | 29.743 | 3.6327 | 0.0000 | 368.9713 | 61.89180 | 8.4887 | 199.7866 | 48.5294 | 37.5793 | 40.4475 | 463.2883 | 73.5536 | 13.2430 | 30.8643 | 0.0000 | 246.7762 | 0.0000 | 359.0444 | 130.6350 | 820.7900 | 194.4371 | 0.0000 | 0.0000 | 0.0000 | 141.4365 | 0.0000 | 0.0 | 1.3526 | 408.798 | 74.640 | 0.7193 | 16.00 | 1.33 | 0.1745 | 264.272 | 0.6510 | 5.16 | 535.0164 | 2.4335 | 5.92 | 0.2653 | 208.2045 | -1.0 |
| 2 | 2932.61 | 2559.94 | 2186.4111 | 1698.0172 | 1.5102 | 95.4878 | 202.0179 | 9.5157 | 416.7075 | 9.31440 | 192.7035 | 12.5404 | -5447.75 | 2701.75 | -4047.00 | -1916.50 | 1.3122 | 67.1333 | 2.3333 | 3.5986 | 84.7569 | 8.6590 | 50.1530 | 64.1114 | 49.8470 | 65.8389 | 84.7327 | 118.6128 | 14.37 | 5.434 | 364.3782 | 9.8783 | 131.8027 | 1.2992 | 141.0845 | 185.7574 | 2936.0 | 23.8245 | 10.1685 | 115.6273 | 11.3019 | 16.1755 | 0.8694 | 145.8000 | 84.7681 | 0.0 | 7.1041 | 1685.8514 | 9317.1698 | -0.1343 | 718.5777 | 58.4808 | 6.4527 | 2.882 | 0.8798 | 3.094 | 3.272 | -0.1892 | 998.4440 | 42.0579 | 89.0 | 126.4 | 96.5 | 45.1001 | 306.0380 | 8.33 | 4.819 | 8.4430 | 0.47 | 562.0 | 788.0 | 759.0 | 2100.0 | 0.187 | 2.1 | 1.4 | 0.319 | 1.0824 | 0.9677 | 0.326 | 17.78 | 0.745 | 13.31 | 22.912 | 0.1959 | 9.21 | 60.110 | 0.139 | 0.949 | 16.73 | 5.09 | 79.618 | 2.4266 | 182.4956 | 839.6006 | 104.4042 | 0.4166 | 29.621 | 3.9133 | 0.0000 | 0.0000 | 50.64250 | 18.7546 | 109.5747 | 60.0000 | 70.9161 | 32.3594 | 21.3645 | 148.0287 | 45.5423 | 13.3923 | 434.2674 | 151.7665 | 0.0000 | 190.3869 | 746.9150 | 74.0741 | 191.7582 | 250.1742 | 0.0000 | 0.0000 | 240.7767 | 244.2748 | 0.0 | 0.7942 | 411.136 | 74.654 | 0.1832 | 16.16 | 0.85 | 0.3718 | 267.064 | 0.9032 | 1.10 | 535.0245 | 2.0293 | 11.21 | 0.1882 | 82.8602 | 1.0 |
| 3 | 2988.72 | 2479.90 | 2199.0333 | 909.7926 | 1.3204 | 104.2367 | 201.8482 | 9.6052 | 422.2894 | 9.69240 | 192.1557 | 12.4782 | -5468.25 | 2648.25 | -4515.00 | -1657.25 | 1.3137 | 62.9333 | 2.6444 | 3.3813 | 84.9105 | 8.6789 | 50.5100 | 64.1125 | 49.4900 | 65.1951 | 86.6867 | 117.0442 | 76.90 | 1.279 | 363.0273 | 9.9305 | 131.8027 | 1.3027 | 142.5427 | 189.9079 | 2936.0 | 24.3791 | 10.2112 | 116.1818 | 13.5597 | 15.6209 | 0.9761 | 147.6545 | 70.2289 | 0.0 | 7.5925 | 1752.0968 | 8205.7000 | 0.0411 | 709.0867 | 58.6635 | 6.4935 | 3.132 | 1.3660 | 2.480 | 3.119 | 0.2838 | 980.4510 | 41.1025 | 127.0 | 118.0 | 123.7 | 47.8000 | 162.4320 | 5.51 | 9.073 | 15.2410 | 0.35 | 859.0 | 355.0 | 3433.0 | 3004.0 | 0.068 | 1.7 | 0.9 | 0.241 | 0.9386 | 0.8567 | 0.390 | 16.22 | 0.693 | 14.67 | 22.562 | 0.1786 | 5.69 | 52.571 | 0.139 | 1.264 | 13.56 | 5.92 | 104.950 | 5.5398 | 152.0885 | 820.3999 | 94.0954 | 0.4212 | 31.830 | 3.1959 | 317.7362 | 0.0000 | 94.45940 | 76.0354 | 181.2641 | 34.0336 | 41.5236 | 27.6824 | 24.2831 | 100.0021 | 48.4887 | 35.4323 | 225.0169 | 100.4883 | 305.7500 | 88.5553 | 104.6660 | 71.7583 | 0.0000 | 336.7660 | 0.0000 | 711.6418 | 113.5593 | 0.0000 | 0.0 | 1.1650 | 372.822 | 72.442 | 1.8804 | 131.68 | 39.33 | 0.7288 | 268.228 | 0.6511 | 7.32 | 530.5682 | 2.0253 | 9.33 | 0.1738 | 73.8432 | -1.0 |
| 4 | 3032.24 | 2502.87 | 2233.3667 | 1326.5200 | 1.5334 | 100.3967 | 201.9424 | 10.5661 | 420.5925 | 10.33870 | 191.6037 | 12.4735 | -5476.25 | 2635.25 | -3987.50 | 117.00 | 1.2887 | 62.8333 | 3.1556 | 3.2728 | 86.3269 | 8.7677 | 50.2480 | 64.1511 | 49.7520 | 66.1542 | 86.1468 | 121.4364 | 76.39 | 2.209 | 353.3400 | 10.4091 | 176.3136 | 1.0341 | 138.0882 | 233.5491 | 2865.0 | -12.2945 | 9.7948 | 144.0191 | 21.9782 | 32.2945 | 0.9256 | 146.6636 | 65.8417 | 0.0 | 7.5017 | 1828.3846 | 9014.4600 | 0.2189 | 796.5950 | 58.3858 | 6.3551 | 3.148 | 0.9460 | 3.027 | 3.299 | -0.5677 | 993.1274 | 38.1448 | 119.0 | 143.2 | 123.1 | 48.8000 | 296.3030 | 3.64 | 9.005 | 12.5060 | 0.43 | 699.0 | 283.0 | 1747.0 | 1443.0 | 0.147 | 3.9 | 0.8 | 0.499 | 0.5760 | 0.8285 | 0.922 | 15.24 | 0.282 | 10.85 | 37.715 | 0.1189 | 3.98 | 72.149 | 0.250 | 0.519 | 19.77 | 5.52 | 92.307 | 4.1338 | 69.1510 | 1406.4004 | 149.2172 | 0.4051 | 19.862 | 3.6163 | 0.0000 | 866.0295 | 85.22550 | 43.8119 | 0.0000 | 25.3521 | 37.4691 | 30.8924 | 44.8980 | 89.9529 | 19.1303 | 42.6838 | 171.4486 | 276.8810 | 461.8619 | 240.1781 | 0.0000 | 587.3773 | 748.1781 | 0.0000 | 293.1396 | 0.0000 | 148.0663 | 0.0000 | 0.0 | 1.4636 | 399.914 | 79.156 | 1.0388 | 19.63 | 1.98 | 0.2156 | 264.272 | 0.6510 | 5.16 | 532.0155 | 2.0275 | 8.83 | 0.2224 | 73.8432 | -1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1562 | 2899.41 | 2464.36 | 2179.7333 | 3085.3781 | 1.4843 | 82.2467 | 203.9867 | 11.7692 | 419.3404 | 10.23970 | 193.7470 | 12.5373 | -5418.75 | 2608.00 | -6228.25 | 356.00 | 1.2817 | 71.1444 | 2.2222 | 3.4680 | 83.8405 | 8.7164 | 50.2482 | 64.3573 | 49.7518 | 66.2013 | 86.3672 | 120.5156 | 81.21 | 1.427 | 369.9009 | 10.3149 | 128.6527 | 1.0736 | 138.0482 | 56.3603 | 2864.0 | 2.8182 | 10.7991 | 111.4709 | 9.4918 | 17.1818 | 0.9923 | 147.2109 | 100.7715 | 0.0 | 7.7716 | 1869.4215 | 9201.7201 | -0.0382 | 727.6761 | 58.3514 | 6.4295 | 4.947 | 1.5410 | 2.454 | 3.135 | 0.0000 | 997.7594 | 41.9860 | 212.0 | 96.1 | 155.0 | 32.3999 | 1635.3000 | 9.59 | 8.107 | 6.8190 | 0.23 | 1280.0 | 334.0 | 7112.0 | 565.0 | 0.123 | 5.9 | 2.2 | 0.585 | 0.7184 | 0.6572 | 0.159 | 23.23 | 0.783 | 13.18 | 14.104 | 0.1506 | 6.46 | 29.690 | 0.088 | 0.369 | 12.71 | 7.67 | 65.531 | 5.1355 | 134.6324 | 526.1006 | 54.6842 | 0.1713 | 14.205 | 3.3907 | 708.6657 | 984.6154 | 68.88330 | 114.1894 | 158.7079 | 99.0000 | 78.1369 | 52.6790 | 272.1613 | 133.9050 | 40.1105 | 12.1317 | 293.5185 | 148.3175 | 138.7755 | 112.9534 | 249.9270 | 112.2755 | 348.5294 | 219.4872 | 0.0000 | 0.0000 | 53.1915 | 235.7895 | 0.0 | 1.2960 | 401.774 | 77.166 | 1.1040 | 16.90 | 0.80 | 0.3553 | 264.272 | 0.5671 | 4.98 | 536.3418 | 2.0153 | 7.98 | 0.2363 | 203.1720 | -1.0 |
| 1563 | 3052.31 | 2522.55 | 2198.5667 | 1124.6595 | 0.8763 | 98.4689 | 204.0173 | 9.1620 | 405.8178 | 10.22850 | 193.7889 | 12.4020 | -6408.75 | 2277.50 | -3675.50 | 339.00 | 1.0870 | 72.8444 | 2.0000 | 4.7088 | 84.0623 | 8.9607 | 50.2067 | 64.1176 | 49.7934 | 66.1488 | 86.4051 | 120.2552 | 79.43 | 2.945 | 351.4055 | 9.7453 | 151.0709 | 0.8566 | 132.9045 | 158.2730 | 2829.0 | -3.3555 | 10.1602 | 127.7155 | 13.2491 | 23.3555 | 0.4904 | 144.7736 | 131.2343 | 0.0 | 7.4522 | 1872.5133 | 8624.8599 | -0.0821 | 755.7527 | 57.1020 | 6.2902 | 1.671 | 1.3090 | 2.783 | 3.805 | -0.1419 | 1015.7622 | 42.9394 | 88.0 | 235.3 | 219.8 | 59.2000 | 355.0260 | 6.86 | 7.625 | 6.9530 | 0.28 | 504.0 | 94.0 | 315.0 | 367.0 | 0.055 | 2.7 | 0.7 | 0.081 | 0.6214 | 0.6833 | 0.414 | 20.52 | 0.580 | 7.24 | 30.347 | 0.0736 | 10.30 | 29.344 | 0.249 | 0.595 | 24.47 | 4.67 | 151.826 | 3.3349 | 171.2301 | 406.5000 | 47.9077 | 0.3786 | 24.500 | 4.5432 | 764.0816 | 612.3324 | 83.22420 | 8.5703 | 108.2596 | 35.0000 | 43.7991 | 18.5401 | 139.1762 | 184.6921 | 17.3265 | 28.7030 | 293.5185 | 148.3175 | 138.7755 | 112.9534 | 249.9270 | 112.2755 | 348.5294 | 219.4872 | 816.3636 | 874.5098 | 29.4372 | 700.0000 | 0.0 | 0.8273 | 400.814 | 73.254 | 0.2235 | 14.53 | 1.33 | 0.3105 | 266.832 | 0.6254 | 4.56 | 537.9264 | 2.1814 | 5.48 | 0.3891 | 203.1720 | -1.0 |
| 1564 | 2978.81 | 2379.78 | 2206.3000 | 1110.4967 | 0.8236 | 99.4122 | 199.5356 | 8.9670 | 412.2191 | 9.85175 | 189.6642 | 12.4555 | -5153.25 | 2707.00 | -4102.00 | -1226.00 | 1.2930 | 71.2667 | 2.2333 | 3.4912 | 85.8638 | 8.1728 | 50.9333 | 64.4062 | 49.0667 | 65.8936 | 86.3506 | 117.8912 | 82.03 | 2.863 | 350.3145 | 9.9572 | 134.8609 | 1.3337 | 142.8818 | 216.6705 | 2804.0 | 1.1664 | 10.3880 | 116.0273 | 10.6773 | 18.8336 | 0.9078 | 152.8191 | 97.2315 | 0.0 | 7.7772 | 1820.3629 | 8992.6702 | 0.2516 | 704.2686 | 59.2046 | 6.2790 | 3.877 | 1.1440 | 2.735 | 3.195 | -0.1419 | 1004.0500 | 38.9026 | 85.0 | 146.9 | 134.0 | 73.4001 | 300.2620 | 7.59 | 5.951 | 10.9935 | 0.29 | 1178.0 | 542.0 | 3662.0 | 1355.0 | 0.109 | 3.2 | 1.9 | 0.529 | 0.6512 | 0.8963 | 0.790 | 20.07 | 0.515 | 8.74 | 20.963 | 0.1666 | 4.06 | 81.803 | 0.205 | 0.175 | 19.37 | 8.15 | 59.503 | 3.1351 | 100.3093 | 1089.0996 | 117.3369 | 0.3431 | 17.977 | 3.7035 | 302.1776 | 272.4487 | 69.90545 | 89.2735 | 110.5220 | 85.0746 | 91.0248 | 37.7546 | 698.7529 | 181.4129 | 43.7209 | 6.8976 | 293.5185 | 148.3175 | 138.7755 | 112.9534 | 249.9270 | 112.2755 | 348.5294 | 219.4872 | 456.7164 | 0.0000 | 54.8330 | 0.0000 | 0.0 | 0.8997 | 391.040 | 74.156 | 0.6671 | 16.07 | 1.50 | 0.1266 | 256.730 | 0.8209 | 11.09 | 530.3709 | 2.3435 | 6.49 | 0.4154 | 43.5231 | -1.0 |
| 1565 | 2894.92 | 2532.01 | 2177.0333 | 1183.7287 | 1.5726 | 98.7978 | 197.2448 | 9.7354 | 401.9153 | 9.86300 | 187.3818 | 12.3937 | -5271.75 | 2676.50 | -4001.50 | 394.75 | 1.2875 | 70.5111 | 2.9667 | 3.2803 | 84.5602 | 9.1930 | 50.6547 | 64.0158 | 49.3453 | 66.2301 | 86.3130 | 118.9288 | 81.13 | 2.067 | 370.5845 | 10.3144 | 127.4127 | 1.0855 | 141.9164 | 69.6385 | 2883.0 | 4.4682 | 10.8024 | 111.8809 | 7.7718 | 15.5318 | 0.9981 | 152.3909 | 96.2286 | 0.0 | 7.7508 | 1627.4714 | 8825.4351 | -0.0926 | 605.6190 | 58.2686 | 6.2416 | 4.682 | 1.3850 | 2.658 | 3.381 | -0.9934 | 999.4826 | 38.6689 | 107.0 | 103.0 | 66.6 | 63.6001 | 365.1101 | 6.76 | 8.253 | 26.1280 | 0.24 | 1740.0 | 252.0 | 2702.0 | 1093.0 | 0.098 | 2.2 | 1.4 | 0.690 | 0.3993 | 0.6709 | 0.271 | 26.63 | 0.980 | 14.75 | 13.879 | 0.0846 | 5.23 | 20.392 | 0.523 | 0.459 | 9.49 | 7.32 | 58.463 | 3.2586 | 89.1078 | 967.2998 | 99.4298 | 0.4089 | 21.870 | 4.1069 | 470.7506 | 0.0000 | 84.77340 | 67.5247 | 276.8841 | 47.1910 | 69.5855 | 29.2827 | 163.8250 | 122.1087 | 44.5347 | 9.5410 | 293.5185 | 148.3175 | 138.7755 | 112.9534 | 249.9270 | 112.2755 | 348.5294 | 219.4872 | 511.3402 | 433.3952 | 78.4993 | 456.4103 | 0.0 | 0.8273 | 400.814 | 73.254 | 0.2235 | 14.53 | 1.33 | 0.1920 | 264.272 | 0.5671 | 4.98 | 534.3936 | 1.9098 | 9.13 | 0.3669 | 93.4941 | -1.0 |
| 1566 | 2944.92 | 2450.76 | 2195.4444 | 2914.1792 | 1.5978 | 85.1011 | 199.5356 | 8.9670 | 412.2191 | 9.85175 | 189.6642 | 12.4790 | -5319.50 | 2668.00 | -3951.75 | -425.00 | 1.3020 | 73.0667 | 2.5889 | 3.3860 | 83.3424 | 8.7786 | 50.1940 | 64.2226 | 49.8060 | 66.2251 | 86.4039 | 120.5418 | 80.45 | 2.741 | 362.8100 | 10.0451 | 142.4364 | 0.8728 | 134.0300 | 135.5562 | 2874.0 | 1.8718 | 10.4236 | 124.3082 | 8.5542 | 18.1282 | 0.9683 | 146.8064 | 125.9159 | 0.0 | 7.5935 | 1759.9908 | 7952.4100 | -0.0728 | 683.5622 | 59.8578 | 6.4084 | 3.877 | 1.1440 | 2.735 | 3.195 | -0.1419 | 1004.0500 | 38.9026 | 112.0 | 203.3 | 68.1 | 22.0000 | 627.6799 | 5.94 | 5.951 | 10.9935 | 0.23 | 763.0 | 304.0 | 503.0 | 1797.0 | 0.051 | 3.9 | 1.3 | 0.370 | 0.6821 | 0.8099 | 0.465 | 23.35 | 0.718 | 15.74 | 18.859 | 0.1327 | 6.59 | 23.171 | 0.181 | 0.841 | 16.09 | 12.32 | 96.624 | 4.2218 | 225.3136 | 1213.0996 | 121.7717 | 0.2669 | 17.977 | 3.7035 | 302.1776 | 272.4487 | 69.90545 | 12.7285 | 422.8235 | 50.2146 | 88.0704 | 17.0933 | 658.1836 | 188.0953 | 76.2409 | 20.0799 | 293.5185 | 148.3175 | 138.7755 | 112.9534 | 249.9270 | 112.2755 | 348.5294 | 219.4872 | 0.0000 | 0.0000 | 75.8621 | 317.6471 | 0.0 | 0.8273 | 400.814 | 73.254 | 0.2235 | 14.53 | 1.33 | 0.2327 | 257.974 | 0.6193 | 8.42 | 528.7918 | 2.0831 | 6.81 | 0.4774 | 137.7844 | -1.0 |
1567 rows × 140 columns
- The minimum.
- Q1 (the first quartile, or the 25% mark).
- The median (50%).
- Q3 (the third quartile, or the 75% mark).
- The maximum.
print('''\n\033[1m''' + '''Statistical Analysis of dataset''' + '''\033[0m''')
sd.describe()
Statistical Analysis of dataset
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 449 | 450 | 451 | 452 | 453 | 454 | 455 | 456 | 457 | 458 | 459 | 460 | 461 | 462 | 463 | 464 | 465 | 466 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 481 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 493 | 494 | 495 | 496 | 497 | 498 | 499 | 500 | 501 | 502 | 503 | 504 | 505 | 506 | 507 | 508 | 509 | 510 | 511 | 512 | 513 | 514 | 515 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 528 | 529 | 530 | 531 | 532 | 533 | 534 | 535 | 536 | 537 | 538 | 539 | 540 | 541 | 542 | 543 | 544 | 545 | 546 | 547 | 548 | 549 | 550 | 551 | 552 | 553 | 554 | 555 | 556 | 557 | 558 | 559 | 560 | 561 | 562 | 563 | 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.00000 | 1567.000000 | 1567.000000 | 1567.00000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.00000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.00000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.0 | 1567.00000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 |
| mean | 3014.441551 | 2495.866110 | 2200.551958 | 1395.383474 | 4.171281 | 100.0 | 101.116476 | 0.121825 | 1.462860 | -0.000842 | 0.000146 | 0.964355 | 199.956272 | 0.0 | 9.005297 | 413.084376 | 9.907496 | 0.971446 | 190.046620 | 12.481152 | 1.405054 | -5618.272176 | 2699.333280 | -3806.318177 | -298.317538 | 1.203946 | 1.938538 | 6.639427 | 69.499093 | 2.366212 | 0.184162 | 3.67288 | 85.337340 | 8.960157 | 50.58252 | 64.555538 | 49.417489 | 66.221280 | 86.836566 | 118.679376 | 68.063966 | 3.348792 | 70.0 | 355.537743 | 10.031167 | 136.742841 | 733.672669 | 1.178005 | 139.972254 | 1.0 | 632.253633 | 157.437518 | 0.0 | 4.592979 | 4.838534 | 2856.166560 | 0.928855 | 0.949215 | 4.593259 | 2.951249 | 355.153887 | 10.423195 | 116.501216 | 13.986604 | 20.539783 | 27.127928 | 706.667700 | 16.655186 | 147.438189 | 1.0 | 619.101422 | 104.322429 | 0.002677 | -0.006894 | -0.029383 | -0.007085 | -0.013625 | 0.003415 | -0.018380 | -0.021130 | 0.006079 | 7.452076 | 0.133107 | 2.401872 | 0.982420 | 1807.815021 | 0.188749 | 8827.468461 | 0.002431 | 0.000507 | -0.000540 | -0.000029 | 0.000060 | 0.017076 | 0.0 | -0.018073 | 0.001534 | -0.000021 | -0.000007 | 0.001110 | -0.009789 | -0.000015 | -0.000497 | 0.000538 | -0.001759 | -0.010790 | 0.459962 | 0.945424 | 0.000123 | 747.383792 | 0.987130 | 58.625908 | 0.598421 | 0.970777 | 6.310863 | 15.796388 | 3.898267 | 15.829662 | 15.794620 | 1.184720 | 2.750638 | 0.648509 | 3.192198 | -0.551860 | 0.745056 | 0.997808 | 2.318513 | 1004.043129 | 39.389480 | 117.932355 | 138.180983 | 122.670645 | 57.587810 | 416.077185 | 25.847024 | 0.0 | 6.638156 | 0.004168 | 0.119992 | 0.063615 | 0.055004 | 0.017409 | 8.470601 | 0.0 | 6.812615 | 14.041556 | 1.195339 | 0.011925 | 7.697616 | 0.505977 | 0.058089 | 882.349075 | 555.196554 | 4064.996171 | 4793.308870 | 0.140179 | 0.127893 | 0.251939 | 2.788641 | 1.235737 | 0.124390 | 0.400468 | 0.684331 | 0.120059 | 0.320115 | 0.576193 | 0.320115 | 0.778037 | 0.244717 | 0.394699 | 0.0 | 0.0 | 19.013050 | 0.546756 | 10.780153 | 26.661515 | 0.144808 | 7.365338 | 0.0 | 17.936244 | 43.209503 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.287013 | 8.679962 | 20.089943 | 0.556848 | 11.520715 | 17.598561 | 7.834537 | 10.162831 | 30.073234 | 32.095412 | 9.045105 | 0.001276 | 20.373663 | 73.264254 | 0.029451 | 0.088725 | 0.056701 | 0.051281 | 0.060280 | 0.083148 | 0.081097 | 0.083501 | 0.071483 | 3.771376 | 0.003252 | 0.060718 | 0.008821 | 122.846571 | 0.058734 | 1038.656080 | 0.0 | 0.019122 | 0.017841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.004791 | 0.004575 | 0.0 | 0.0 | 0.0 | 0.0 | 0.041340 | 0.025171 | 0.001065 | 109.650967 | 0.004285 | 4.645115 | 0.033179 | 0.013943 | 0.403848 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.070587 | 19.496877 | 3.777486 | 29.258737 | 46.055143 | 41.292434 | 20.175883 | 136.062895 | 8.616246 | 0.0 | 2.209604 | 0.001117 | 0.041052 | 0.018032 | 0.015093 | 0.005769 | 2.803798 | 0.0 | 2.119327 | 4.258295 | 0.367092 | 0.003923 | 2.578539 | 0.123171 | 0.019926 | 401.657580 | 252.926146 | 1878.364532 | 2340.883483 | 0.063790 | 0.060241 | 0.118341 | 0.910082 | 0.403314 | 0.040342 | 0.132083 | 0.264916 | 0.048621 | 0.128921 | 0.218415 | 0.128922 | 0.304751 | 0.097345 | 0.160026 | 0.0 | 0.0 | 0.0 | 5.976884 | 0.172623 | 3.188586 | 7.916338 | 0.043103 | 2.263609 | 0.0 | 5.393462 | 13.331644 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.083236 | 2.591113 | 6.214963 | 0.168215 | 3.423428 | 9.733425 | 2.326262 | 3.035450 | 9.329514 | 14.617500 | 2.730735 | 0.000285 | 6.197770 | 23.217137 | 0.008880 | 0.024673 | 0.025233 | 0.023135 | 0.027549 | 0.023335 | 0.040358 | 0.041956 | 0.034464 | 1.298601 | 0.000998 | 0.019840 | 0.002945 | 39.936406 | 0.018191 | 332.555156 | 0.0 | 0.005198 | 0.004813 | 0.003771 | 0.003170 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.001601 | 0.001571 | 0.0 | 0.0 | 0.0 | 0.0 | 0.009316 | 0.008281 | 0.000339 | 35.155091 | 0.001338 | 1.431868 | 0.010944 | 0.004533 | 0.133990 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.024206 | 6.726477 | 1.232037 | 5.339597 | 4.580083 | 4.928466 | 2.615479 | 30.868839 | 25.564824 | 0.0 | 6.62656 | 3.403370 | 8.189890 | 320.236157 | 309.014570 | 1.821037 | 4.174228 | 0.0 | 77.645599 | 3.314227 | 6.792422 | 1.233669 | 4.058277 | 4.209984 | 4.171844 | 18.412262 | 22.350676 | 99.314795 | 205.449868 | 14.728156 | 9.364514 | 7.507205 | 4.016484 | 54.693892 | 70.637297 | 11.527330 | 0.802084 | 1.345207 | 0.633947 | 0.895048 | 0.647092 | 1.174996 | 0.281894 | 0.332218 | 0.0 | 0.0 | 0.0 | 5.346767 | 5.460822 | 7.883449 | 3.636690 | 12.325063 | 5.263366 | 0.0 | 2.838375 | 29.19548 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.250541 | 223.843226 | 5.661566 | 5.362749 | 9.630209 | 137.890044 | 39.403707 | 37.615532 | 4.262626 | 20.116232 | 6.254032 | 0.127632 | 3.283016 | 75.518589 | 0.0 | 318.037084 | 205.672096 | 214.117076 | 199.761505 | 301.700889 | 237.507454 | 352.553880 | 271.362828 | 51.352261 | 2.441204 | 2.530046 | 0.956442 | 6.807826 | 29.611163 | 11.791267 | 0.0 | 262.859941 | 240.673807 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 55.752306 | 275.627217 | 0.0 | 0.0 | 0.0 | 0.0 | 9.038411 | 2.695999 | 11.610080 | 14.728866 | 0.453896 | 5.687782 | 5.553870 | 1.443457 | 6.395717 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.03434 | 1.942025 | 9.610850 | 0.111206 | 0.008470 | 0.002509 | 7.610771 | 1.018304 | 403.476047 | 75.415081 | 0.631357 | 16.901595 | 1.217320 | 0.263615 | 7.658083 | 0.499550 | 57.198192 | 4.192049 | 1.607594 | 0.994995 | 0.325686 | 0.072435 | 32.283326 | 262.998383 | 0.674651 | 6.221117 | 0.141069 | 2.530656 | 0.058094 | 2.373545 | 20.398270 | 530.523623 | 2.101836 | 28.450165 | 0.345636 | 9.162315 | 0.104729 | 5.563747 | 16.642363 | 0.500096 | 0.015317 | 0.003846 | 3.067628 | 0.021458 | 0.016474 | 0.005283 | 99.652345 | -0.867262 |
| std | 73.480841 | 80.228143 | 29.380973 | 439.837330 | 56.103721 | 0.0 | 6.209385 | 0.008936 | 0.073849 | 0.015107 | 0.009296 | 0.012444 | 3.255230 | 0.0 | 2.793916 | 17.204633 | 2.401564 | 0.012051 | 2.778426 | 0.217273 | 0.016737 | 626.430997 | 295.312481 | 1379.280633 | 2900.846582 | 0.177510 | 0.189382 | 1.243655 | 3.458992 | 0.408433 | 0.032923 | 0.53505 | 2.025908 | 1.344036 | 1.18225 | 2.573945 | 1.182251 | 0.304044 | 0.446613 | 1.806658 | 23.911897 | 2.342518 | 0.0 | 6.232884 | 0.174982 | 7.846746 | 12.166430 | 0.189585 | 4.522806 | 0.0 | 8.641254 | 60.909166 | 0.0 | 0.054880 | 0.059505 | 25.716644 | 0.006800 | 0.004171 | 0.084992 | 9.511839 | 6.016917 | 0.274351 | 8.612494 | 7.104106 | 4.966452 | 7.106006 | 11.600798 | 306.914183 | 4.231976 | 0.0 | 9.520899 | 31.591384 | 0.105986 | 0.022121 | 0.032948 | 0.031129 | 0.047504 | 0.022905 | 0.048862 | 0.016891 | 0.035797 | 0.516087 | 0.005032 | 0.037332 | 0.012848 | 53.537262 | 0.051514 | 389.807042 | 0.087515 | 0.003229 | 0.003008 | 0.000174 | 0.000104 | 0.219159 | 0.0 | 0.426292 | 0.062620 | 0.000355 | 0.000220 | 0.062847 | 0.003063 | 0.000850 | 0.003196 | 0.002982 | 0.087307 | 0.086591 | 0.036174 | 0.012133 | 0.001668 | 48.949250 | 0.009497 | 6.485174 | 0.008040 | 0.008949 | 0.124304 | 0.099332 | 0.901520 | 0.108003 | 0.113821 | 0.279764 | 0.252744 | 0.135020 | 0.263414 | 1.217366 | 0.082300 | 0.002244 | 0.053047 | 6.520981 | 2.983032 | 57.454912 | 53.806875 | 52.137163 | 12.291096 | 262.221743 | 504.656946 | 0.0 | 3.536522 | 0.001278 | 0.061305 | 0.026524 | 0.021831 | 0.027106 | 18.728671 | 0.0 | 3.238956 | 30.973119 | 23.341694 | 0.009337 | 5.234204 | 1.118937 | 0.079174 | 982.458855 | 574.456703 | 4236.854877 | 6550.267099 | 0.121913 | 0.242383 | 0.407076 | 1.119061 | 0.632364 | 0.047609 | 0.197792 | 0.157418 | 0.060766 | 0.071220 | 0.095703 | 0.071224 | 0.116285 | 0.074894 | 0.282823 | 0.0 | 0.0 | 3.310585 | 0.224331 | 4.162749 | 6.833931 | 0.110163 | 7.186442 | 0.0 | 8.607162 | 21.705075 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.394684 | 15.686267 | 10.532022 | 0.536737 | 16.415041 | 8.671307 | 5.093583 | 14.590631 | 17.428314 | 564.021108 | 11.519237 | 0.050524 | 17.464051 | 28.013323 | 1.165835 | 0.041756 | 0.024817 | 0.031358 | 0.052624 | 0.056030 | 0.030203 | 0.025566 | 0.045944 | 1.170068 | 0.001640 | 0.023305 | 0.055937 | 55.156003 | 0.070128 | 426.259257 | 0.0 | 0.010749 | 0.010738 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.001697 | 0.001440 | 0.0 | 0.0 | 0.0 | 0.0 | 0.051023 | 0.049235 | 0.015771 | 54.597274 | 0.037472 | 64.354756 | 0.022255 | 0.009132 | 0.120334 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.029573 | 7.326430 | 1.149394 | 8.388634 | 17.832193 | 17.698031 | 3.813721 | 85.258562 | 168.194470 | 0.0 | 1.191138 | 0.000339 | 0.020277 | 0.006479 | 0.005542 | 0.008545 | 5.860580 | 0.0 | 0.962059 | 9.754552 | 7.379271 | 0.002933 | 1.615444 | 0.270139 | 0.025549 | 476.765634 | 283.356947 | 1973.997717 | 3225.321542 | 0.064185 | 0.130744 | 0.219011 | 0.331774 | 0.197389 | 0.014501 | 0.064826 | 0.057369 | 0.025392 | 0.027459 | 0.033582 | 0.027461 | 0.043446 | 0.028787 | 0.117282 | 0.0 | 0.0 | 0.0 | 1.018311 | 0.072370 | 1.215564 | 2.178396 | 0.031875 | 2.116324 | 0.0 | 2.518056 | 6.613770 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.063344 | 5.632706 | 3.396952 | 0.172568 | 5.770748 | 7.539354 | 1.695730 | 5.632481 | 6.063060 | 261.238119 | 3.660934 | 0.011297 | 5.361538 | 8.878164 | 0.351511 | 0.011774 | 0.010523 | 0.014226 | 0.024376 | 0.013057 | 0.015382 | 0.012970 | 0.022144 | 0.386796 | 0.000499 | 0.007136 | 0.020003 | 17.056304 | 0.021314 | 136.586680 | 0.0 | 0.002654 | 0.002381 | 0.002694 | 0.002103 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000534 | 0.000466 | 0.0 | 0.0 | 0.0 | 0.0 | 0.010838 | 0.015488 | 0.004989 | 17.227003 | 0.011816 | 20.326415 | 0.006687 | 0.002956 | 0.038408 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.010701 | 2.822938 | 0.363779 | 2.574107 | 1.773445 | 2.118268 | 0.549041 | 18.336590 | 47.099220 | 0.0 | 3.94087 | 1.032534 | 4.052025 | 287.521430 | 325.243132 | 3.055746 | 6.909444 | 0.0 | 32.567460 | 6.319367 | 23.235597 | 0.994675 | 3.039233 | 10.599579 | 6.435390 | 36.037997 | 36.372786 | 126.116776 | 225.643014 | 34.087451 | 34.348266 | 34.536146 | 1.610266 | 34.086853 | 38.352115 | 6.165562 | 0.184154 | 0.658987 | 0.143506 | 0.155472 | 0.141207 | 0.176102 | 0.086433 | 0.236209 | 0.0 | 0.0 | 0.0 | 0.918905 | 2.250093 | 3.058705 | 0.938075 | 8.123318 | 4.536303 | 0.0 | 1.345147 | 13.33115 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 8.662693 | 230.303247 | 3.145663 | 4.974466 | 10.155553 | 47.591323 | 22.409521 | 24.769461 | 2.606167 | 14.913155 | 10.165690 | 5.052374 | 2.633556 | 35.685329 | 0.0 | 278.866442 | 191.514533 | 211.695946 | 217.277824 | 285.226689 | 262.273846 | 250.105428 | 226.384996 | 18.042987 | 1.219698 | 0.973948 | 6.615200 | 3.260019 | 24.257158 | 4.877969 | 0.0 | 324.699975 | 322.911797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 37.668964 | 329.601505 | 0.0 | 0.0 | 0.0 | 0.0 | 12.022316 | 5.702366 | 103.122996 | 7.104435 | 4.147581 | 20.663414 | 3.890565 | 0.958428 | 1.888698 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.24971 | 0.730143 | 2.888989 | 0.002736 | 0.001533 | 0.000295 | 1.314823 | 0.358508 | 4.627143 | 3.152734 | 0.619061 | 4.542840 | 1.243363 | 0.253958 | 2.005003 | 0.546962 | 32.175775 | 1.170259 | 1.708471 | 0.083835 | 0.201329 | 0.051562 | 19.020115 | 6.958289 | 0.111169 | 2.442203 | 0.074373 | 0.954554 | 0.030083 | 0.921761 | 9.411775 | 17.499736 | 0.275112 | 86.304681 | 0.248478 | 26.920150 | 0.067791 | 16.921369 | 12.485267 | 0.003403 | 0.017174 | 0.003719 | 3.576899 | 0.012354 | 0.008805 | 0.002866 | 93.864558 | 0.498010 |
| min | 2743.240000 | 2158.750000 | 2060.660000 | 0.000000 | 0.681500 | 100.0 | 82.131100 | 0.000000 | 1.191000 | -0.053400 | -0.034900 | 0.655400 | 182.094000 | 0.0 | 2.249300 | 333.448600 | 4.469600 | 0.579400 | 169.177400 | 9.877300 | 1.179700 | -7150.250000 | 0.000000 | -9986.750000 | -14804.500000 | 0.000000 | 0.000000 | 0.000000 | 59.400000 | 0.666700 | 0.034100 | 2.06980 | 83.182900 | 7.603200 | 49.83480 | 63.677400 | 40.228900 | 64.919300 | 84.732700 | 111.712800 | 1.434000 | -0.075900 | 70.0 | 342.754500 | 9.464000 | 108.846400 | 699.813900 | 0.496700 | 125.798200 | 1.0 | 607.392700 | 40.261400 | 0.0 | 3.706000 | 3.932000 | 2801.000000 | 0.875500 | 0.931900 | 4.219900 | -28.988200 | 324.714500 | 9.461100 | 81.490000 | 1.659100 | 6.448200 | 4.308000 | 632.422600 | 0.413700 | 87.025500 | 1.0 | 581.777300 | 21.433200 | 0.000000 | -0.104900 | -0.186200 | -0.104600 | -0.348200 | -0.056800 | -0.143700 | -0.098200 | -0.212900 | 5.825700 | 0.117400 | 2.242500 | 0.774900 | 1627.471400 | 0.111300 | 7397.310000 | -0.357000 | -0.012600 | -0.017100 | -0.002000 | -0.000900 | -1.480300 | 0.0 | -5.271700 | -0.528300 | -0.003000 | -0.002400 | -0.535300 | -0.032900 | -0.011900 | -0.028100 | -0.013300 | -0.522600 | -0.345400 | 0.000000 | 0.853400 | 0.000000 | 544.025400 | 0.890000 | 52.806800 | 0.527400 | 0.841100 | 5.125900 | 15.460000 | 1.671000 | 15.170000 | 15.430000 | 0.312200 | 2.340000 | 0.316100 | 0.000000 | -3.779000 | 0.419900 | 0.993600 | 2.191100 | 980.451000 | 33.365800 | 58.000000 | 36.100000 | 19.200000 | 19.800000 | 0.000000 | 0.031900 | 0.0 | 1.740000 | 0.000000 | 0.032400 | 0.021400 | 0.022700 | 0.004300 | 1.420800 | 0.0 | 1.337000 | 2.020000 | 0.154400 | 0.003600 | 1.243800 | 0.140000 | 0.011100 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.800000 | 0.300000 | 0.033000 | 0.046000 | 0.297900 | 0.008900 | 0.128700 | 0.253800 | 0.128700 | 0.461600 | 0.073500 | 0.047000 | 0.0 | 0.0 | 9.400000 | 0.093000 | 3.170000 | 5.014000 | 0.029700 | 1.940000 | 0.0 | 6.220000 | 6.613000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.080000 | 1.750000 | 9.220000 | 0.090000 | 2.770000 | 3.210000 | 0.000000 | 0.000000 | 7.728000 | 0.042900 | 2.300000 | 0.000000 | 4.010000 | 5.359000 | 0.000000 | 0.031900 | 0.002200 | 0.007100 | 0.003700 | 0.019300 | 0.005900 | 0.009700 | 0.007900 | 1.034000 | 0.000700 | 0.020000 | 0.000300 | 32.263700 | 0.009300 | 168.799800 | 0.0 | 0.006200 | 0.007200 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.001300 | 0.001400 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.003000 | 0.000000 | 21.010700 | 0.000300 | 0.767300 | 0.009400 | 0.001700 | 0.126900 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.019800 | 6.098000 | 1.301700 | 15.547100 | 10.401500 | 6.943100 | 8.651200 | 0.000000 | 0.011100 | 0.0 | 0.561500 | 0.000000 | 0.010700 | 0.007300 | 0.006900 | 0.001600 | 0.505000 | 0.0 | 0.461100 | 0.728000 | 0.051300 | 0.001200 | 0.396000 | 0.041600 | 0.003800 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.310000 | 0.111800 | 0.010800 | 0.013800 | 0.117100 | 0.003400 | 0.054900 | 0.091300 | 0.054900 | 0.180900 | 0.032800 | 0.022400 | 0.0 | 0.0 | 0.0 | 2.788200 | 0.028300 | 0.984800 | 1.657400 | 0.008400 | 0.611400 | 0.0 | 1.710100 | 2.234500 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.022400 | 0.537300 | 2.837200 | 0.028200 | 0.789900 | 5.215100 | 0.000000 | 0.000000 | 2.200100 | 0.013100 | 0.574100 | 0.000000 | 1.256500 | 2.056000 | 0.000000 | 0.010300 | 0.001000 | 0.002900 | 0.002000 | 0.005600 | 0.002600 | 0.004000 | 0.003800 | 0.379600 | 0.000300 | 0.007600 | 0.000100 | 10.720400 | 0.002800 | 60.988200 | 0.0 | 0.001700 | 0.002000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000400 | 0.000400 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000800 | 0.000000 | 6.310100 | 0.000100 | 0.304600 | 0.003100 | 0.000500 | 0.034200 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.006200 | 2.054500 | 0.424000 | 2.737800 | 1.216300 | 0.734200 | 0.960900 | 0.000000 | 4.041600 | 0.0 | 1.53400 | 0.000000 | 2.153100 | 0.000000 | 0.000000 | 0.441100 | 0.721700 | 0.0 | 23.020000 | 0.486600 | 1.466600 | 0.363200 | 0.663700 | 1.119800 | 0.783700 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.156800 | 0.000000 | 14.120600 | 1.097300 | 0.351200 | 0.097400 | 0.216900 | 0.333600 | 0.308600 | 0.696800 | 0.084600 | 0.039900 | 0.0 | 0.0 | 0.0 | 2.670900 | 0.903700 | 2.329400 | 0.694800 | 3.048900 | 1.442800 | 0.0 | 0.991000 | 7.95340 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.716300 | 0.000000 | 2.600900 | 0.832500 | 2.402600 | 11.499700 | 0.000000 | 0.000000 | 1.101100 | 0.000000 | 1.687200 | 0.000000 | 0.645900 | 8.840600 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 13.722500 | 0.555800 | 0.833000 | 0.034200 | 1.772000 | 4.813500 | 1.949600 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.312100 | 0.000000 | 2.681100 | 0.025800 | 1.310400 | 1.540000 | 0.170500 | 2.170000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.85160 | 0.614400 | 3.276100 | 0.105300 | 0.005100 | 0.001600 | 4.429400 | 0.444400 | 372.822000 | 71.038000 | 0.044600 | 6.110000 | 0.120000 | 0.018700 | 2.786000 | 0.052000 | 4.826900 | 1.496700 | 0.164600 | 0.891900 | 0.069900 | 0.017700 | 7.236900 | 242.286000 | 0.304900 | 0.970000 | 0.022400 | 0.412200 | 0.009100 | 0.370600 | 3.250400 | 317.196400 | 0.980200 | 3.540000 | 0.066700 | 1.039500 | 0.023000 | 0.663600 | 4.582000 | 0.477800 | 0.006000 | 0.001700 | 1.197500 | -0.016900 | 0.003200 | 0.001000 | 0.000000 | -1.000000 |
| 25% | 2966.665000 | 2452.885000 | 2181.099950 | 1083.885800 | 1.017700 | 100.0 | 97.937800 | 0.121100 | 1.411250 | -0.010800 | -0.005600 | 0.958100 | 198.130950 | 0.0 | 7.096750 | 406.131000 | 9.568550 | 0.968250 | 188.300650 | 12.460000 | 1.396500 | -5932.625000 | 2578.125000 | -4370.625000 | -1474.375000 | 1.094900 | 1.906750 | 5.267350 | 67.383350 | 2.088900 | 0.161800 | 3.36270 | 84.490500 | 8.580000 | 50.25290 | 64.024800 | 49.421900 | 66.040800 | 86.578300 | 118.015600 | 74.840000 | 2.699000 | 70.0 | 350.802250 | 9.925450 | 130.730450 | 724.444000 | 0.985000 | 136.930000 | 1.0 | 625.928650 | 115.537450 | 0.0 | 4.574000 | 4.816000 | 2836.000000 | 0.925500 | 0.946700 | 4.531900 | -1.855450 | 350.606850 | 10.284050 | 112.055450 | 10.383650 | 17.377300 | 23.074450 | 698.796550 | 0.891500 | 145.242300 | 1.0 | 612.783650 | 87.584600 | 0.000000 | -0.019200 | -0.051350 | -0.029400 | -0.047300 | -0.010700 | -0.042950 | -0.027100 | -0.017350 | 7.104350 | 0.129800 | 2.376850 | 0.975800 | 1777.470300 | 0.169750 | 8578.569950 | -0.042650 | -0.001150 | -0.001600 | -0.000100 | 0.000000 | -0.088400 | 0.0 | -0.217300 | -0.029800 | -0.000200 | -0.000100 | -0.035300 | -0.011800 | -0.000400 | -0.001900 | -0.001000 | -0.048350 | -0.064400 | 0.462400 | 0.938600 | 0.000000 | 721.023000 | 0.989500 | 57.978300 | 0.594200 | 0.964800 | 6.246400 | 15.730000 | 3.202000 | 15.770000 | 15.730000 | 0.974400 | 2.574000 | 0.548900 | 3.076000 | -0.898800 | 0.688700 | 0.996400 | 2.277300 | 1000.045450 | 37.368900 | 92.000000 | 90.150000 | 81.400000 | 51.000000 | 243.841100 | 0.131800 | 0.0 | 5.110000 | 0.003300 | 0.083900 | 0.048050 | 0.042350 | 0.010000 | 6.362800 | 0.0 | 4.465500 | 8.096000 | 0.373850 | 0.007300 | 5.931600 | 0.240000 | 0.036250 | 411.500000 | 295.000000 | 1322.500000 | 451.000000 | 0.091000 | 0.068500 | 0.132000 | 2.100000 | 0.900000 | 0.090000 | 0.230500 | 0.575600 | 0.079800 | 0.276600 | 0.516900 | 0.276500 | 0.692200 | 0.196300 | 0.222000 | 0.0 | 0.0 | 16.850000 | 0.378000 | 7.735000 | 21.172000 | 0.102200 | 5.390000 | 0.0 | 14.510000 | 24.714000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.219500 | 5.045000 | 17.150000 | 0.296000 | 6.745000 | 14.175000 | 5.025000 | 6.101500 | 24.667000 | 0.114300 | 6.050000 | 0.000000 | 16.365000 | 56.220500 | 0.000000 | 0.065900 | 0.043950 | 0.032600 | 0.036950 | 0.057050 | 0.063600 | 0.069800 | 0.046100 | 2.946200 | 0.002300 | 0.040200 | 0.001400 | 95.147350 | 0.030050 | 726.500000 | 0.0 | 0.013250 | 0.012650 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.003700 | 0.003600 | 0.0 | 0.0 | 0.0 | 0.0 | 0.025150 | 0.014700 | 0.000000 | 76.132150 | 0.000700 | 2.205650 | 0.024600 | 0.004700 | 0.307600 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.044000 | 13.828000 | 2.962400 | 24.982300 | 30.043050 | 27.101500 | 18.279250 | 81.247400 | 0.044800 | 0.0 | 1.698550 | 0.000900 | 0.028300 | 0.014200 | 0.011900 | 0.003300 | 2.211500 | 0.0 | 1.440150 | 2.467600 | 0.114950 | 0.002400 | 2.092950 | 0.065200 | 0.012500 | 185.111450 | 130.263200 | 603.290800 | 211.254550 | 0.040700 | 0.030200 | 0.058950 | 0.717400 | 0.295800 | 0.030000 | 0.072800 | 0.225000 | 0.033100 | 0.113700 | 0.197600 | 0.113700 | 0.278600 | 0.077600 | 0.091500 | 0.0 | 0.0 | 0.0 | 5.302150 | 0.117450 | 2.320050 | 6.247800 | 0.031200 | 1.670350 | 0.0 | 4.273400 | 7.578800 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.068800 | 1.546800 | 5.456600 | 0.089700 | 2.036500 | 8.292150 | 1.544400 | 1.902800 | 7.593400 | 0.034750 | 1.914250 | 0.000000 | 5.007700 | 17.941050 | 0.000000 | 0.018200 | 0.019700 | 0.014600 | 0.016600 | 0.016100 | 0.030500 | 0.035300 | 0.021400 | 1.025650 | 0.000700 | 0.013800 | 0.000400 | 32.168700 | 0.009500 | 230.073100 | 0.0 | 0.003800 | 0.003550 | 0.002600 | 0.002200 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.001300 | 0.001300 | 0.0 | 0.0 | 0.0 | 0.0 | 0.006500 | 0.004800 | 0.000000 | 24.386550 | 0.000200 | 0.675150 | 0.008350 | 0.001500 | 0.104400 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.014100 | 4.559800 | 0.966500 | 4.127800 | 3.019000 | 3.269450 | 2.325300 | 18.470300 | 11.474550 | 0.0 | 4.92880 | 2.667850 | 5.767150 | 0.000000 | 0.000000 | 1.030950 | 3.184850 | 0.0 | 56.008200 | 1.965450 | 3.767150 | 0.744200 | 3.113950 | 1.936500 | 2.571400 | 7.006200 | 11.069050 | 31.033850 | 10.047450 | 7.551150 | 3.495000 | 1.951700 | 3.071700 | 36.343700 | 48.177350 | 5.418350 | 0.679600 | 0.908800 | 0.550500 | 0.804800 | 0.555800 | 1.046800 | 0.226100 | 0.187700 | 0.0 | 0.0 | 0.0 | 4.765400 | 3.748750 | 5.808150 | 2.899750 | 8.817150 | 3.828650 | 0.0 | 2.291350 | 20.22410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.697500 | 38.882650 | 4.848500 | 2.824000 | 5.821600 | 105.622150 | 24.933350 | 23.185450 | 3.496850 | 11.585350 | 4.111500 | 0.000000 | 2.629900 | 52.971050 | 0.0 | 0.000000 | 82.410150 | 77.011800 | 51.188500 | 0.000000 | 57.316900 | 145.156850 | 113.806650 | 38.393700 | 1.747100 | 1.663750 | 0.139000 | 5.274600 | 16.486550 | 8.272500 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 35.324400 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 5.430050 | 1.552150 | 0.000000 | 10.182800 | 0.073050 | 3.769650 | 4.117100 | 0.484200 | 4.895450 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.88990 | 1.390600 | 7.504200 | 0.109600 | 0.007800 | 0.002400 | 7.116000 | 0.814100 | 400.814000 | 73.254000 | 0.289600 | 14.820000 | 0.910000 | 0.116700 | 6.738100 | 0.375000 | 34.147100 | 3.654100 | 1.224200 | 0.955200 | 0.149950 | 0.036200 | 15.766900 | 262.101000 | 0.567100 | 4.980000 | 0.087700 | 2.090200 | 0.038200 | 1.884400 | 15.466200 | 530.702700 | 1.982900 | 7.500000 | 0.242250 | 2.567850 | 0.075100 | 1.408450 | 11.501550 | 0.497900 | 0.011600 | 0.003100 | 2.306500 | 0.013450 | 0.010600 | 0.003300 | 44.368600 | -1.000000 |
| 50% | 3011.490000 | 2499.405000 | 2201.066700 | 1285.214400 | 1.316800 | 100.0 | 101.512200 | 0.122400 | 1.461600 | -0.001300 | 0.000400 | 0.965800 | 199.535600 | 0.0 | 8.967000 | 412.219100 | 9.851750 | 0.972600 | 189.664200 | 12.499600 | 1.406000 | -5523.250000 | 2664.000000 | -3820.750000 | -78.750000 | 1.283000 | 1.986500 | 7.264700 | 69.155600 | 2.377800 | 0.186700 | 3.43100 | 85.135450 | 8.769800 | 50.39640 | 64.165800 | 49.603600 | 66.231800 | 86.820700 | 118.399300 | 78.290000 | 3.074000 | 70.0 | 353.720900 | 10.034850 | 136.400000 | 733.450000 | 1.251050 | 140.007750 | 1.0 | 631.370900 | 183.318150 | 0.0 | 4.596000 | 4.843000 | 2854.000000 | 0.931000 | 0.949300 | 4.572700 | 0.947250 | 353.799100 | 10.436700 | 116.211800 | 13.246050 | 20.021350 | 26.261450 | 706.453600 | 0.978300 | 147.597300 | 1.0 | 619.032700 | 102.604300 | 0.000000 | -0.006300 | -0.028900 | -0.009900 | -0.012500 | 0.000600 | -0.008700 | -0.019600 | 0.007600 | 7.467450 | 0.133000 | 2.403900 | 0.987400 | 1809.249200 | 0.190100 | 8825.435100 | 0.000000 | 0.000400 | -0.000200 | 0.000000 | 0.000000 | 0.003900 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -0.010100 | 0.000000 | -0.000200 | 0.000200 | 0.000000 | -0.011200 | 0.462850 | 0.946400 | 0.000000 | 750.861400 | 0.990500 | 58.549100 | 0.599000 | 0.969400 | 6.313600 | 15.790000 | 3.877000 | 15.830000 | 15.780000 | 1.144000 | 2.735000 | 0.653900 | 3.195000 | -0.141900 | 0.758750 | 0.997750 | 2.312400 | 1004.050000 | 38.902600 | 109.000000 | 134.600000 | 117.700000 | 55.900100 | 339.561000 | 0.235800 | 0.0 | 6.260000 | 0.003900 | 0.107500 | 0.058600 | 0.050000 | 0.015900 | 7.917300 | 0.0 | 5.951000 | 10.993500 | 0.468700 | 0.011100 | 7.512700 | 0.320000 | 0.048700 | 623.000000 | 438.000000 | 2614.000000 | 1784.000000 | 0.120000 | 0.089000 | 0.184000 | 2.600000 | 1.200000 | 0.119000 | 0.412000 | 0.686000 | 0.112500 | 0.323850 | 0.577600 | 0.323850 | 0.768200 | 0.242900 | 0.299000 | 0.0 | 0.0 | 18.690000 | 0.524000 | 10.170000 | 27.200500 | 0.132600 | 6.735000 | 0.0 | 17.865000 | 40.209500 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.259000 | 6.780000 | 19.370000 | 0.424000 | 8.570000 | 17.235000 | 6.760000 | 8.462000 | 30.097000 | 0.158200 | 7.740000 | 0.000000 | 19.720000 | 73.248000 | 0.000000 | 0.079700 | 0.053200 | 0.041600 | 0.056000 | 0.075400 | 0.082500 | 0.084600 | 0.061700 | 3.630750 | 0.003000 | 0.060900 | 0.002300 | 119.436000 | 0.039800 | 967.299800 | 0.0 | 0.016500 | 0.015500 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.004600 | 0.004400 | 0.0 | 0.0 | 0.0 | 0.0 | 0.027000 | 0.021000 | 0.000000 | 103.093600 | 0.001000 | 2.864600 | 0.030800 | 0.015000 | 0.405100 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.070600 | 17.977000 | 3.703500 | 28.773500 | 45.676500 | 40.019250 | 19.580900 | 110.601400 | 0.078400 | 0.0 | 2.083100 | 0.001100 | 0.037200 | 0.016900 | 0.013900 | 0.005300 | 2.658000 | 0.0 | 1.875150 | 3.360050 | 0.138950 | 0.003600 | 2.549000 | 0.083300 | 0.016900 | 278.671900 | 195.825600 | 1202.412100 | 820.098800 | 0.052800 | 0.040000 | 0.082800 | 0.860400 | 0.380800 | 0.038800 | 0.137200 | 0.264300 | 0.044800 | 0.129500 | 0.219450 | 0.129500 | 0.302900 | 0.097700 | 0.121500 | 0.0 | 0.0 | 0.0 | 5.831500 | 0.163400 | 2.898900 | 8.388800 | 0.039850 | 2.077650 | 0.0 | 5.458800 | 12.504500 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.084800 | 2.062700 | 5.980100 | 0.129400 | 2.513500 | 9.073550 | 2.054450 | 2.560850 | 9.474200 | 0.046400 | 2.377300 | 0.000000 | 6.005600 | 23.214700 | 0.000000 | 0.022600 | 0.024000 | 0.018800 | 0.025300 | 0.022000 | 0.042100 | 0.044200 | 0.029400 | 1.255300 | 0.000900 | 0.019600 | 0.000700 | 39.696100 | 0.012500 | 309.831650 | 0.0 | 0.004600 | 0.004300 | 0.003200 | 0.002800 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.001600 | 0.001500 | 0.0 | 0.0 | 0.0 | 0.0 | 0.006800 | 0.006800 | 0.000000 | 32.530700 | 0.000300 | 0.877300 | 0.010200 | 0.004900 | 0.133900 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.023900 | 5.920100 | 1.239700 | 4.922450 | 4.489700 | 4.732750 | 2.548100 | 26.156900 | 20.255100 | 0.0 | 6.17660 | 3.234000 | 7.395600 | 302.177600 | 272.448700 | 1.645100 | 3.943100 | 0.0 | 69.905450 | 2.667100 | 4.764400 | 1.135300 | 3.941450 | 2.534100 | 3.453800 | 11.105600 | 16.381000 | 57.969300 | 151.115600 | 10.197700 | 4.551100 | 2.764300 | 3.780900 | 49.090900 | 65.437800 | 12.085900 | 0.807600 | 1.264550 | 0.643500 | 0.902700 | 0.651100 | 1.163800 | 0.279700 | 0.251200 | 0.0 | 0.0 | 0.0 | 5.271450 | 5.227100 | 7.424900 | 3.724500 | 11.350900 | 4.793350 | 0.0 | 2.830350 | 26.16785 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5.645000 | 150.340100 | 5.472400 | 4.061100 | 7.396000 | 138.255150 | 34.246750 | 32.820050 | 4.276200 | 15.973800 | 5.242200 | 0.000000 | 3.184500 | 70.434500 | 0.0 | 293.518500 | 148.317500 | 138.775500 | 112.953400 | 249.927000 | 112.275500 | 348.529400 | 219.487200 | 48.557450 | 2.250800 | 2.529100 | 0.232500 | 6.607900 | 22.039100 | 10.906550 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 46.986100 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 5.832950 | 2.221000 | 0.000000 | 13.742600 | 0.100000 | 4.877100 | 5.134200 | 1.550100 | 6.410800 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.05480 | 1.785500 | 9.459300 | 0.109600 | 0.007800 | 0.002600 | 7.116000 | 0.911100 | 403.122000 | 74.084000 | 0.471000 | 16.340000 | 1.150000 | 0.197900 | 7.427900 | 0.478900 | 54.441700 | 4.067100 | 1.529800 | 0.972700 | 0.290900 | 0.059200 | 29.731150 | 264.272000 | 0.651000 | 5.160000 | 0.119550 | 2.150450 | 0.048650 | 1.999700 | 16.988350 | 532.398200 | 2.118600 | 8.650000 | 0.293400 | 2.975800 | 0.089500 | 1.624500 | 13.817900 | 0.500200 | 0.013800 | 0.003600 | 2.757650 | 0.020500 | 0.014800 | 0.004600 | 71.900500 | -1.000000 |
| 75% | 3056.540000 | 2538.745000 | 2218.055500 | 1590.169900 | 1.518800 | 100.0 | 104.530000 | 0.123800 | 1.516850 | 0.008400 | 0.005900 | 0.971300 | 202.006750 | 0.0 | 10.858700 | 419.082800 | 10.127750 | 0.976800 | 192.178900 | 12.547100 | 1.415000 | -5356.625000 | 2840.625000 | -3356.375000 | 1376.250000 | 1.304300 | 2.003200 | 7.329600 | 72.255550 | 2.655600 | 0.207000 | 3.53125 | 85.741900 | 9.060600 | 50.57810 | 64.344700 | 49.747100 | 66.343050 | 87.002400 | 118.939600 | 80.180000 | 3.515000 | 70.0 | 360.771800 | 10.152450 | 142.090950 | 741.449500 | 1.340200 | 143.194100 | 1.0 | 638.128150 | 206.976700 | 0.0 | 4.617000 | 4.869000 | 2874.000000 | 0.933100 | 0.952000 | 4.668600 | 4.337700 | 359.665450 | 10.590550 | 120.918200 | 16.325500 | 22.799550 | 29.907350 | 714.521050 | 1.064900 | 149.935900 | 1.0 | 625.160450 | 115.439600 | 0.000000 | 0.006600 | -0.006900 | 0.008900 | 0.012050 | 0.012800 | 0.008700 | -0.012150 | 0.026800 | 7.807350 | 0.136300 | 2.428600 | 0.989700 | 1841.873000 | 0.200150 | 9055.260000 | 0.050350 | 0.002000 | 0.001000 | 0.000100 | 0.000100 | 0.121200 | 0.0 | 0.188650 | 0.029800 | 0.000200 | 0.000100 | 0.033600 | -0.008200 | 0.000400 | 0.001100 | 0.001550 | 0.048600 | 0.037850 | 0.463400 | 0.952300 | 0.000000 | 776.781850 | 0.990900 | 59.133900 | 0.603300 | 0.978300 | 6.375850 | 15.860000 | 4.392000 | 15.900000 | 15.870000 | 1.338000 | 2.873000 | 0.712400 | 3.311000 | 0.047300 | 0.814500 | 0.998900 | 2.358300 | 1008.670600 | 40.804600 | 127.000000 | 180.900000 | 161.600000 | 62.900100 | 494.806000 | 0.434800 | 0.0 | 7.500000 | 0.004900 | 0.132650 | 0.071800 | 0.061500 | 0.021300 | 9.581850 | 0.0 | 8.269500 | 14.342000 | 0.677500 | 0.014900 | 9.050950 | 0.450000 | 0.066700 | 963.500000 | 624.500000 | 5033.000000 | 6376.000000 | 0.154000 | 0.116000 | 0.254500 | 3.200000 | 1.500000 | 0.150500 | 0.536000 | 0.797300 | 0.140300 | 0.370200 | 0.634500 | 0.370200 | 0.843900 | 0.293750 | 0.423000 | 0.0 | 0.0 | 20.965000 | 0.688500 | 13.335000 | 31.687000 | 0.169100 | 8.450000 | 0.0 | 20.860000 | 57.672500 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.296000 | 9.510000 | 21.450000 | 0.724500 | 11.425000 | 20.160000 | 9.485000 | 11.931950 | 33.504500 | 0.230600 | 9.930000 | 0.000000 | 22.365000 | 90.452500 | 0.000000 | 0.099050 | 0.064100 | 0.062100 | 0.073400 | 0.093250 | 0.098100 | 0.097300 | 0.086050 | 4.403400 | 0.003800 | 0.076500 | 0.005500 | 144.502800 | 0.059200 | 1252.399650 | 0.0 | 0.021200 | 0.020000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.005700 | 0.005300 | 0.0 | 0.0 | 0.0 | 0.0 | 0.030100 | 0.027300 | 0.000000 | 131.758400 | 0.001300 | 3.795050 | 0.037700 | 0.021300 | 0.480950 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.091600 | 24.653000 | 4.376800 | 31.691650 | 59.484150 | 54.166500 | 22.089100 | 161.979100 | 0.144900 | 0.0 | 2.513100 | 0.001300 | 0.045750 | 0.020700 | 0.016600 | 0.007100 | 3.145700 | 0.0 | 2.605900 | 4.306600 | 0.198400 | 0.004900 | 3.023800 | 0.117550 | 0.023600 | 428.515800 | 273.865150 | 2337.950650 | 3188.190750 | 0.069200 | 0.052000 | 0.115350 | 1.046400 | 0.476700 | 0.048600 | 0.178500 | 0.307300 | 0.055100 | 0.147600 | 0.237900 | 0.147600 | 0.331900 | 0.115900 | 0.160150 | 0.0 | 0.0 | 0.0 | 6.546900 | 0.218100 | 4.021200 | 9.480800 | 0.050200 | 2.632900 | 0.0 | 6.344350 | 17.925150 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.095600 | 2.789300 | 6.544500 | 0.210150 | 3.355500 | 10.034700 | 2.779950 | 3.397900 | 10.438250 | 0.066800 | 2.984400 | 0.000000 | 6.881050 | 28.852650 | 0.000000 | 0.027300 | 0.028500 | 0.028100 | 0.033600 | 0.026800 | 0.050100 | 0.049900 | 0.041900 | 1.533250 | 0.001100 | 0.025000 | 0.001800 | 47.079200 | 0.018300 | 409.116550 | 0.0 | 0.005800 | 0.005400 | 0.004200 | 0.003600 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.001900 | 0.001800 | 0.0 | 0.0 | 0.0 | 0.0 | 0.007150 | 0.009300 | 0.000000 | 42.652450 | 0.000400 | 1.148200 | 0.012400 | 0.006900 | 0.160400 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.032300 | 8.563000 | 1.416700 | 5.787100 | 5.929950 | 6.453800 | 2.848450 | 38.026900 | 29.307300 | 0.0 | 7.54530 | 4.010700 | 9.167850 | 523.624450 | 582.803100 | 2.214600 | 4.783200 | 0.0 | 92.832750 | 3.469200 | 6.873850 | 1.538850 | 4.767950 | 3.609000 | 4.755800 | 17.402050 | 21.739650 | 120.136900 | 304.541800 | 12.752800 | 5.822150 | 3.821250 | 4.678250 | 66.666700 | 84.944500 | 15.795850 | 0.927600 | 1.577550 | 0.733250 | 0.988800 | 0.748400 | 1.272300 | 0.338650 | 0.351100 | 0.0 | 0.0 | 0.0 | 5.912900 | 6.898750 | 9.576750 | 4.341750 | 14.387400 | 6.089400 | 0.0 | 3.308950 | 35.26840 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.386900 | 334.674000 | 6.003200 | 6.991150 | 9.707900 | 168.270250 | 47.690150 | 45.160350 | 4.741050 | 23.682400 | 6.699750 | 0.000000 | 3.624550 | 92.911650 | 0.0 | 512.390750 | 260.079000 | 288.918450 | 283.289000 | 497.384500 | 391.277500 | 507.497050 | 372.341900 | 61.489950 | 2.830000 | 3.199100 | 0.563000 | 7.897200 | 31.961500 | 14.337300 | 0.0 | 536.122600 | 505.225750 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 64.228450 | 554.010700 | 0.0 | 0.0 | 0.0 | 0.0 | 6.468900 | 2.903700 | 0.000000 | 17.808950 | 0.133200 | 6.450650 | 6.301850 | 2.211650 | 7.594250 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.94180 | 2.454500 | 11.200850 | 0.113400 | 0.009000 | 0.002600 | 8.008950 | 1.216500 | 406.763000 | 76.960000 | 0.773400 | 18.405000 | 1.330000 | 0.321750 | 8.316650 | 0.533550 | 69.630650 | 4.564950 | 1.815600 | 1.000800 | 0.443600 | 0.089000 | 44.113400 | 264.733000 | 0.738250 | 7.310000 | 0.166850 | 2.909150 | 0.068100 | 2.807750 | 23.035200 | 534.356400 | 2.290650 | 10.130000 | 0.366900 | 3.492500 | 0.112150 | 1.902000 | 17.080900 | 0.502350 | 0.016500 | 0.004100 | 3.294950 | 0.027600 | 0.020300 | 0.006400 | 114.749700 | -1.000000 |
| max | 3356.350000 | 2846.440000 | 2315.266700 | 3715.041700 | 1114.536600 | 100.0 | 129.252200 | 0.128600 | 1.656400 | 0.074900 | 0.053000 | 0.984800 | 272.045100 | 0.0 | 19.546500 | 824.927100 | 102.867700 | 0.984800 | 215.597700 | 12.989800 | 1.453400 | 0.000000 | 3656.250000 | 2363.000000 | 14106.000000 | 1.382800 | 2.052800 | 7.658800 | 77.900000 | 3.511100 | 0.285100 | 4.80440 | 105.603800 | 23.345300 | 59.77110 | 94.264100 | 50.165200 | 67.958600 | 88.418800 | 133.389800 | 86.120000 | 37.880000 | 70.0 | 377.297300 | 11.053000 | 176.313600 | 789.752300 | 1.511100 | 163.250900 | 1.0 | 667.741800 | 258.543200 | 0.0 | 4.764000 | 5.011000 | 2936.000000 | 0.937800 | 0.959800 | 4.847500 | 168.145500 | 373.866400 | 11.784900 | 287.150900 | 188.092300 | 48.988200 | 118.083600 | 770.608400 | 7272.828300 | 167.830900 | 1.0 | 722.601800 | 238.477500 | 4.195500 | 0.231500 | 0.072300 | 0.133100 | 0.249200 | 0.101300 | 0.118600 | 0.058400 | 0.143700 | 8.990400 | 0.150500 | 2.555500 | 0.993500 | 2105.182300 | 1.472700 | 10746.600000 | 0.362700 | 0.028100 | 0.013300 | 0.001100 | 0.000900 | 2.509300 | 0.0 | 2.569800 | 0.885400 | 0.002300 | 0.001700 | 0.297900 | 0.020300 | 0.007100 | 0.012700 | 0.017200 | 0.485600 | 0.393800 | 0.488500 | 0.976300 | 0.041400 | 924.531800 | 0.992400 | 311.734400 | 0.624500 | 0.982700 | 7.522000 | 16.070000 | 6.889000 | 16.100000 | 16.100000 | 2.465000 | 3.991000 | 1.175000 | 3.895000 | 2.458000 | 0.888400 | 1.019000 | 2.472300 | 1020.994400 | 64.128700 | 994.000000 | 295.800000 | 334.700000 | 141.799800 | 1770.690900 | 9998.894400 | 0.0 | 103.390000 | 0.012100 | 0.625300 | 0.250700 | 0.247900 | 0.978300 | 742.942100 | 0.0 | 22.318000 | 536.564000 | 924.378000 | 0.238900 | 191.547800 | 12.710000 | 2.201600 | 7791.000000 | 4170.000000 | 37943.000000 | 36871.000000 | 0.957000 | 1.817000 | 3.286000 | 21.100000 | 16.300000 | 0.725000 | 1.143000 | 1.153000 | 0.494000 | 0.548400 | 0.864300 | 0.548400 | 1.172000 | 0.441100 | 1.858000 | 0.0 | 0.0 | 48.670000 | 3.573000 | 55.000000 | 72.947000 | 3.228300 | 267.910000 | 0.0 | 307.930000 | 191.830000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.838000 | 396.110000 | 252.870000 | 10.017000 | 390.120000 | 199.620000 | 126.530000 | 490.561000 | 500.349000 | 9998.448300 | 320.050000 | 2.000000 | 457.650000 | 172.349000 | 46.150000 | 0.516400 | 0.322700 | 0.594100 | 1.283700 | 0.761500 | 0.342900 | 0.282800 | 0.674400 | 8.801500 | 0.016300 | 0.230500 | 0.991100 | 1768.880200 | 1.436100 | 3601.299800 | 0.0 | 0.154100 | 0.213300 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.024400 | 0.023600 | 0.0 | 0.0 | 0.0 | 0.0 | 0.491400 | 0.973200 | 0.413800 | 1119.704200 | 0.990900 | 2549.988500 | 0.451700 | 0.078700 | 0.925500 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.157800 | 40.855000 | 10.152900 | 158.526000 | 132.647900 | 122.117400 | 43.573700 | 659.169600 | 3332.596400 | 0.0 | 32.170900 | 0.003400 | 0.188400 | 0.075500 | 0.059700 | 0.308300 | 232.804900 | 0.0 | 6.869800 | 207.016100 | 292.227400 | 0.074900 | 59.518700 | 4.420300 | 0.691500 | 3933.755000 | 2005.874400 | 15559.952500 | 18520.468300 | 0.526400 | 1.031200 | 1.812300 | 5.711000 | 5.154900 | 0.225800 | 0.333700 | 0.475000 | 0.224600 | 0.211200 | 0.323900 | 0.211200 | 0.443800 | 0.178400 | 0.754900 | 0.0 | 0.0 | 0.0 | 13.095800 | 1.003400 | 15.893400 | 20.045500 | 0.947400 | 79.151500 | 0.0 | 89.191700 | 51.867800 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.095900 | 174.894400 | 90.515900 | 3.412500 | 172.711900 | 214.862800 | 38.899500 | 196.688000 | 197.498800 | 5043.878900 | 97.708900 | 0.447200 | 156.336000 | 59.324100 | 13.914700 | 0.220000 | 0.133900 | 0.291400 | 0.618800 | 0.142900 | 0.153500 | 0.134400 | 0.278900 | 2.834800 | 0.005200 | 0.088800 | 0.409000 | 547.172200 | 0.416300 | 1072.203100 | 0.0 | 0.036800 | 0.039200 | 0.035700 | 0.033400 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.008200 | 0.007700 | 0.0 | 0.0 | 0.0 | 0.0 | 0.207300 | 0.306800 | 0.130900 | 348.829300 | 0.312700 | 805.393600 | 0.137500 | 0.022900 | 0.299400 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.051400 | 14.727700 | 3.312800 | 44.310000 | 9.576500 | 13.807100 | 6.215000 | 128.281600 | 899.119000 | 0.0 | 116.86150 | 9.690000 | 39.037600 | 999.316000 | 998.681300 | 111.495600 | 273.095200 | 0.0 | 424.215200 | 103.180900 | 898.608500 | 24.990400 | 113.223000 | 118.753300 | 186.616400 | 400.000000 | 400.000000 | 994.285700 | 995.744700 | 400.000000 | 400.000000 | 400.000000 | 32.274000 | 851.612900 | 657.762100 | 33.058000 | 1.277100 | 5.131700 | 1.085100 | 1.351100 | 1.108700 | 1.763900 | 0.508500 | 1.475400 | 0.0 | 0.0 | 0.0 | 13.977600 | 34.490200 | 42.070300 | 10.184000 | 232.125800 | 164.109300 | 0.0 | 47.777200 | 149.38510 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 109.007400 | 999.877000 | 77.800700 | 87.134700 | 212.655700 | 492.771800 | 358.950400 | 415.435500 | 79.116200 | 274.887100 | 289.826400 | 200.000000 | 63.333600 | 221.974700 | 0.0 | 999.413500 | 989.473700 | 996.858600 | 994.000000 | 999.491100 | 995.744700 | 997.518600 | 994.003500 | 142.843600 | 12.769800 | 9.402400 | 127.572800 | 107.692600 | 219.643600 | 40.281800 | 0.0 | 1000.000000 | 999.233700 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 451.485100 | 1000.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 184.348800 | 111.736500 | 1000.000000 | 137.983800 | 111.333000 | 818.000500 | 80.040600 | 8.203700 | 14.447900 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.58030 | 4.082500 | 25.779200 | 0.118400 | 0.024000 | 0.004700 | 21.044300 | 3.978600 | 421.702000 | 83.720000 | 7.065600 | 131.680000 | 39.330000 | 2.718200 | 56.930300 | 17.478100 | 303.550000 | 35.319800 | 54.291700 | 1.512100 | 1.073700 | 0.445700 | 101.114600 | 311.404000 | 1.298800 | 32.580000 | 0.689200 | 14.014100 | 0.293200 | 12.746200 | 84.802400 | 589.508200 | 2.739500 | 454.560000 | 2.196700 | 170.020400 | 0.550200 | 90.423500 | 96.960100 | 0.509800 | 0.476600 | 0.104500 | 99.303200 | 0.102800 | 0.079900 | 0.028600 | 737.304800 | 1.000000 |
# MAIN outliers
print('''\n\033[1m''' + '''Checking of outliers of each columns:''' + '''\033[0m''')
n=1
for x in sd1:
sd1[x]=sd1[x].astype(float)
att = sd1[x]
mean = np.mean(att)
std = np.std(att)
outlier = []
outliervalue = []
for i in att:
if std != 0:
z = (i-mean)/std
if z < -3.00 or z > 3.00:
outlier.append(i)
outliervalue.append(z)
print('Total no. of outliers in column',x,'are',len(outlier))
print('Outlier Value are',outlier)
sns.boxplot(sd1[x])
plt.xlabel(x)
plt.show()
Checking of outliers of each columns:
Total no. of outliers in column 0 are 16
Outlier Value are [2787.49, 3245.0, 3266.04, 2792.24, 2743.24, 3244.74, 3254.32, 3299.4, 3282.87, 3339.93, 3266.55, 3356.35, 3284.82, 3273.46, 3246.31, 2770.4]
Total no. of outliers in column 1 are 19 Outlier Value are [2748.67, 2788.4, 2809.79, 2739.19, 2810.12, 2162.87, 2786.97, 2226.99, 2187.67, 2186.06, 2158.75, 2221.1, 2846.44, 2254.99, 2233.29, 2839.46, 2744.54, 2806.91, 2815.31]
Total no. of outliers in column 2 are 12 Outlier Value are [2304.2111, 2315.2667, 2315.2667, 2315.2667, 2060.66, 2060.66, 2060.66, 2060.66, 2060.66, 2060.66, 2060.66, 2306.1]
Total no. of outliers in column 3 are 32 Outlier Value are [2941.8341, 3715.0417, 3619.7397, 3619.7397, 3619.7397, 0.0, 3355.2007, 3355.2007, 2945.8855, 2914.1792, 3530.2362, 2945.8855, 2882.8558, 2945.8855, 2945.8855, 3530.2362, 3530.2362, 3085.3781, 3530.2362, 2882.8558, 3530.2362, 3085.3781, 2837.8788, 2882.8558, 3530.2362, 2837.8788, 2914.1792, 2914.1792, 3085.3781, 2945.8855, 3085.3781, 2914.1792]
Total no. of outliers in column 4 are 4 Outlier Value are [1112.16, 1112.0769, 1114.5366, 1112.4728]
Total no. of outliers in column 6 are 20 Outlier Value are [129.2522, 129.2522, 129.2522, 119.9011, 82.1311, 82.1311, 123.4244, 123.4244, 119.9011, 123.4244, 82.2467, 82.2467, 123.41, 119.8822, 119.8822, 119.8822, 123.4244, 119.8822, 82.2467, 82.2467]
Total no. of outliers in column 12 are 5 Outlier Value are [226.0086, 210.5179, 210.618, 272.0451, 182.094]
Total no. of outliers in column 14 are 6 Outlier Value are [17.8597, 18.8626, 18.1408, 19.5465, 18.4695, 18.0388]
Total no. of outliers in column 15 are 4 Outlier Value are [824.9271, 817.003, 333.4486, 504.9312]
Total no. of outliers in column 16 are 1 Outlier Value are [102.8677]
Total no. of outliers in column 18 are 9 Outlier Value are [215.5977, 181.2176, 198.736, 199.1236, 200.1544, 200.3134, 169.1774, 172.1654, 181.1119]
Total no. of outliers in column 19 are 11 Outlier Value are [10.7029, 10.7151, 10.7151, 9.8773, 9.8773, 9.8773, 9.8773, 9.8773, 9.8773, 9.8773, 9.8773]
Total no. of outliers in column 21 are 24 Outlier Value are [-2727.5, -1431.5, -2694.75, -2664.25, -2681.75, -2706.0, -2622.5, -1362.0, -3281.0, -2575.5, -1368.75, -2597.0, -2649.25, -2665.5, -2609.25, -1502.75, -3293.5, -1918.25, -2595.0, -1480.75, -3345.75, -2668.5, 0.0, -1263.5]
Total no. of outliers in column 22 are 27 Outlier Value are [1400.0, 619.5, 1476.25, 1542.0, 1496.25, 1445.75, 1550.0, 710.25, 1523.25, 1544.5, 653.75, 1474.25, 1461.25, 1485.75, 1505.5, 606.5, 1515.5, 736.75, 1455.5, 609.5, 1397.0, 1538.25, 3589.0, 0.0, 3613.75, 820.0, 3656.25]
Total no. of outliers in column 23 are 32 Outlier Value are [1614.75, 908.25, 2363.0, 1932.5, 670.25, 1830.75, -8690.25, 417.0, 1889.25, -8718.6667, -8621.3333, 2287.0, 451.5, 1041.25, 925.3333, 645.75, 398.0, 1401.5, 1094.0, 605.25, -9141.5, 1975.25, -9986.75, 1143.75, 794.5, -8755.6667, -8812.25, -8112.3333, -7964.0, 333.75, 1039.25, -8480.0]
Total no. of outliers in column 24 are 36 Outlier Value are [-14804.5, -9191.5, -10366.333, 10248.75, 9133.0, 11034.75, 9013.5, 10969.3333, 9841.0, 8981.0, -13232.5, 10126.6667, 9022.0, 9092.75, 8777.0, 9055.5, -11705.333, -9440.0, 9329.0, 8902.6667, -11532.25, -14228.667, 9331.3333, 10981.5, 14106.0, 8673.0, 8525.5, 10386.6667, -9228.5, 9503.3333, 13880.0, -10169.0, -9533.6667, -10077.75, 9309.0, -10762.5]
Total no. of outliers in column 25 are 35 Outlier Value are [0.455, 0.2328, 0.4495, 0.4425, 0.443, 0.4515, 0.446, 0.224, 0.4397, 0.6679999999999999, 0.4373, 0.652, 0.2253, 0.6557, 0.4385, 0.441, 0.6565, 0.4525, 0.438, 0.2367, 0.4417, 0.219, 0.6518, 0.4527, 0.2315, 0.4552, 0.4477, 0.0, 0.2122, 0.6438, 0.6268, 0.65, 0.556, 0.6002, 0.657]
Total no. of outliers in column 28 are 0 Outlier Value are []
Total no. of outliers in column 29 are 10 Outlier Value are [0.7111, 0.9889, 0.7667, 0.6667, 1.0333, 1.0333, 0.8778, 1.0667, 1.0444, 0.7667]
Total no. of outliers in column 31 are 0 Outlier Value are []
Total no. of outliers in column 32 are 27 Outlier Value are [101.9072, 101.9072, 101.9072, 101.9072, 92.072, 92.072, 92.072, 92.072, 92.072, 92.072, 92.072, 103.1082, 103.1082, 103.1082, 103.1082, 103.1082, 103.1082, 103.1082, 93.8653, 93.8653, 93.8653, 93.8653, 93.8653, 93.8653, 99.771, 105.6038, 105.6038]
Total no. of outliers in column 33 are 19 Outlier Value are [23.3453, 23.3453, 23.3453, 23.3453, 23.1583, 23.1583, 23.1583, 23.1583, 23.1583, 23.1583, 23.1583, 13.5481, 13.5481, 13.5481, 13.5481, 13.5481, 21.5944, 13.5481, 13.5481]
Total no. of outliers in column 34 are 32 Outlier Value are [57.6663, 57.6663, 57.6663, 57.6663, 59.7711, 59.7711, 59.3488, 59.3488, 59.7711, 59.3488, 59.3488, 59.3488, 59.7711, 59.3488, 59.3488, 59.7711, 57.7421, 57.7421, 57.7421, 57.7421, 57.7421, 57.7421, 57.7421, 57.8485, 57.8485, 57.8485, 57.8485, 57.8485, 57.8485, 58.7862, 58.8024, 58.8024]
Total no. of outliers in column 35 are 38 Outlier Value are [74.7081, 74.7081, 74.7081, 74.7081, 79.2786, 79.2786, 78.25399999999998, 78.25399999999998, 79.2786, 78.25399999999998, 78.25399999999998, 78.25399999999998, 79.2786, 78.25399999999998, 78.25399999999998, 79.2786, 74.8511, 74.8511, 74.8511, 74.8511, 74.8511, 74.8511, 74.8511, 76.2508, 94.2641, 76.2508, 76.2508, 76.2508, 94.2641, 94.2641, 76.2508, 94.2641, 76.2508, 94.2641, 94.2641, 76.4763, 76.1632, 76.1632]
Total no. of outliers in column 36 are 32 Outlier Value are [42.3337, 42.3337, 42.3337, 42.3337, 40.2289, 40.2289, 40.6512, 40.6512, 40.2289, 40.6512, 40.6512, 40.6512, 40.2289, 40.6512, 40.6512, 40.2289, 42.2579, 42.2579, 42.2579, 42.2579, 42.2579, 42.2579, 42.2579, 42.1515, 42.1515, 42.1515, 42.1515, 42.1515, 42.1515, 41.2139, 41.1976, 41.1976]
Total no. of outliers in column 37 are 33 Outlier Value are [64.9193, 65.1951, 65.0665, 65.0665, 65.1951, 65.1111, 65.1951, 65.0894, 64.9193, 65.1893, 65.1893, 65.1902, 64.9193, 65.1893, 64.9193, 65.1893, 65.1902, 65.1893, 67.199, 67.199, 67.1943, 67.199, 67.199, 67.199, 67.1943, 67.199, 67.1943, 67.1943, 67.1943, 67.199, 67.1943, 67.1943, 67.9586]
Total no. of outliers in column 38 are 71 Outlier Value are [84.7327, 84.7327, 88.3312, 88.3312, 88.3312, 88.2425, 85.1414, 88.3548, 88.2847, 88.3548, 88.3548, 84.7327, 88.2074, 88.2425, 88.3312, 88.2425, 88.3282, 88.3282, 88.2425, 88.3282, 88.3548, 88.2207, 88.2109, 88.2847, 88.2425, 85.1414, 88.3548, 88.3282, 88.3282, 88.2109, 88.3282, 88.2074, 85.1414, 88.3548, 88.3312, 88.4188, 88.4188, 88.2109, 88.2207, 88.4188, 88.2109, 88.2207, 88.24, 88.2207, 88.2109, 88.2074, 85.1414, 88.2109, 88.2109, 88.4188, 88.4188, 88.4188, 88.2425, 88.3282, 88.24, 88.2207, 88.2074, 88.4188, 88.24, 88.2207, 88.24, 88.2425, 88.2207, 88.2894, 88.24, 88.24, 88.3312, 88.2074, 88.2894, 88.24, 88.2894]
Total no. of outliers in column 39 are 37 Outlier Value are [111.7128, 126.5662, 126.5662, 126.5662, 126.5662, 111.7128, 111.7128, 111.7128, 133.3898, 133.3898, 132.5378, 132.5378, 133.3898, 132.5378, 132.5378, 132.5378, 133.3898, 124.6244, 132.5378, 132.5378, 133.3898, 127.3944, 127.3944, 127.3944, 127.3944, 127.3944, 127.3944, 127.3944, 125.9348, 125.9348, 125.9348, 125.9348, 125.9348, 125.9348, 125.0222, 126.0786, 126.0786]
Total no. of outliers in column 40 are 0 Outlier Value are []
Total no. of outliers in column 41 are 6 Outlier Value are [37.88, 37.88, 37.88, 37.88, 37.88, 37.88]
Total no. of outliers in column 43 are 6 Outlier Value are [376.0782, 377.2973, 375.2936, 374.8218, 374.9509, 376.4691]
Total no. of outliers in column 44 are 10 Outlier Value are [10.6071, 10.5903, 9.4941, 9.4867, 9.4735, 9.4853, 9.464, 9.4999, 11.053, 10.5994]
Total no. of outliers in column 45 are 10 Outlier Value are [176.3136, 108.8464, 164.5955, 163.1527, 167.1618, 169.0418, 163.6618, 168.7655, 161.0709, 110.3945]
Total no. of outliers in column 47 are 1 Outlier Value are [0.4967]
Total no. of outliers in column 48 are 5 Outlier Value are [125.9727, 125.7982, 126.1864, 154.94, 163.2509]
Total no. of outliers in column 51 are 0 Outlier Value are []
Total no. of outliers in column 55 are 5 Outlier Value are [2936.0, 2936.0, 2936.0, 2936.0, 2936.0]
Total no. of outliers in column 59 are 5 Outlier Value are [87.0391, -28.9882, 168.1455, 36.3682, 53.6818]
Total no. of outliers in column 61 are 16 Outlier Value are [9.5543, 9.5055, 9.4952, 11.496, 11.7063, 9.5748, 11.7849, 9.5798, 9.5577, 9.5044, 11.3067, 9.5335, 9.4611, 11.5887, 9.5052, 9.5718]
Total no. of outliers in column 62 are 14 Outlier Value are [144.0191, 89.8127, 90.29, 90.3318, 81.49, 88.4391, 86.5655, 144.5618, 189.6609, 87.4618, 85.5091, 287.1509, 143.9527, 168.1936]
Total no. of outliers in column 63 are 8 Outlier Value are [39.6172, 38.8027, 40.0636, 89.5305, 35.8527, 188.0923, 53.9451, 37.2708]
Total no. of outliers in column 64 are 23 Outlier Value are [38.4118, 37.4791, 36.0945, 39.1518, 37.9336, 48.3127, 41.2055, 41.5664, 37.4173, 35.5718, 39.5164, 37.9573, 36.72, 37.3591, 38.8827, 35.5318, 41.8473, 38.6282, 36.8864, 48.9882, 38.0518, 42.63, 39.9927]
Total no. of outliers in column 67 are 4 Outlier Value are [6363.6566, 7272.8283, 3637.0928, 6363.8013]
Total no. of outliers in column 68 are 5 Outlier Value are [127.5891, 110.4282, 106.8409, 87.0255, 167.8309]
Total no. of outliers in column 71 are 40 Outlier Value are [204.8898, 200.0261, 211.8448, 222.2734, 221.6792, 209.8135, 224.9449, 207.1709, 210.5112, 218.5524, 231.4128, 225.5875, 227.2577, 222.1145, 227.2456, 238.4775, 224.1387, 223.4975, 213.9842, 209.5703, 233.6113, 227.4135, 229.5238, 199.5758, 220.1348, 211.9558, 212.8519, 202.2698, 204.7133, 202.4137, 214.1869, 220.5295, 202.4391, 203.129, 209.4001, 224.9621, 211.8978, 220.4506, 214.0137, 225.0767]
Total no. of outliers in column 74 are 1 Outlier Value are [4.1955]
Total no. of outliers in column 83 are 2 Outlier Value are [5.8337, 5.8257]
Total no. of outliers in column 88 are 7 Outlier Value are [1632.312, 2105.1823, 1642.5266, 1634.0957, 1633.1937, 1627.4714, 1627.4714]
Total no. of outliers in column 90 are 13 Outlier Value are [7397.31, 10746.6, 10358.1, 10206.58, 7539.29, 7574.11, 7406.64, 10653.5, 10608.8, 10090.3399, 10090.3301, 10137.4601, 10228.08]
Total no. of outliers in column 96 are 23 Outlier Value are [1.895, 0.7972, -1.4803, 0.7696, 1.2055, 0.7164, -1.2004, -0.6587, -0.9229, 0.8166, -0.6788, -1.0008, -0.7025, 1.3082, 2.5093, 0.9914, 0.9144, 0.703, -0.7811, -0.6683, 0.851, -0.7516, 0.728]
Total no. of outliers in column 115 are 15 Outlier Value are [913.7342, 920.359, 914.8248, 544.0254, 598.8947, 581.2285, 598.2087, 571.9780000000002, 584.1758, 909.7511, 595.7959999999998, 600.2219, 924.5318, 900.6217, 909.3752]
Total no. of outliers in column 117 are 1 Outlier Value are [311.7344]
Total no. of outliers in column 120 are 24 Outlier Value are [6.6967, 6.7662, 6.7772, 6.7499, 7.0626, 5.9047, 7.522, 6.7997, 5.8415, 5.7564, 5.8468, 5.9072, 5.7207, 5.7288, 5.1259, 6.6944, 5.8821, 5.8883, 5.9135, 6.7805, 7.4275, 5.9161, 5.9171, 5.9349]
Total no. of outliers in column 122 are 6 Outlier Value are [6.8889999999999985, 6.8889999999999985, 6.8889999999999985, 6.8889999999999985, 6.8889999999999985, 6.8889999999999985]
Total no. of outliers in column 125 are 17 Outlier Value are [0.3122, 0.3122, 0.3122, 0.3122, 0.3122, 2.154, 2.154, 2.154, 2.154, 2.154, 2.154, 2.154, 2.211, 2.211, 2.211, 2.211, 2.465]
Total no. of outliers in column 126 are 26 Outlier Value are [3.955, 3.955, 3.955, 3.955, 3.955, 3.955, 3.955, 3.955, 3.955, 3.635, 3.635, 3.635, 3.811, 3.811, 3.811, 3.811, 3.811, 3.991, 3.991, 3.991, 3.991, 3.991, 3.991, 3.856, 3.856, 3.856]
Total no. of outliers in column 128 are 5 Outlier Value are [0.0, 0.0, 0.0, 0.0, 0.0]
Total no. of outliers in column 129 are 0 Outlier Value are []
Total no. of outliers in column 133 are 4 Outlier Value are [980.451, 984.0379, 984.0379, 984.0379]
Total no. of outliers in column 134 are 5 Outlier Value are [48.5321, 64.1287, 64.1287, 64.1287, 64.1287]
Total no. of outliers in column 135 are 34 Outlier Value are [299.0, 551.0, 371.0, 371.0, 371.0, 551.0, 551.0, 306.0, 371.0, 308.0, 308.0, 593.0, 308.0, 308.0, 308.0, 382.0, 308.0, 308.0, 382.0, 382.0, 593.0, 593.0, 330.0, 330.0, 330.0, 382.0, 380.0, 380.0, 634.0, 330.0, 549.0, 549.0, 302.0, 994.0]
Total no. of outliers in column 136 are 0 Outlier Value are []
Total no. of outliers in column 137 are 3 Outlier Value are [288.2, 334.7, 281.2]
Total no. of outliers in column 138 are 35 Outlier Value are [107.6001, 107.6001, 107.6001, 107.6001, 107.6001, 95.4001, 107.6001, 107.6001, 111.9001, 95.0999, 95.0999, 95.0999, 95.0999, 95.0999, 95.0999, 95.0999, 95.0999, 95.0999, 96.1001, 101.8, 96.2998, 96.2998, 101.8, 101.8, 96.2998, 19.8, 19.8, 19.8, 19.8, 19.8, 19.8, 96.2998, 19.8, 121.1001, 141.7998]
Total no. of outliers in column 139 are 46 Outlier Value are [1245.613, 1245.613, 1245.613, 1245.613, 1245.613, 1245.613, 1245.613, 1245.613, 1738.4641, 1738.4641, 1596.1949, 1738.4641, 1215.093, 1215.093, 1596.1949, 1371.4971, 1407.9781, 1407.9781, 1275.644, 1275.644, 1407.9781, 1321.8339, 1275.644, 1321.8339, 1770.6909, 1321.8339, 1275.644, 1321.8339, 1498.329, 1533.777, 1533.777, 1415.4999, 1533.777, 1533.777, 1415.4999, 1533.777, 1258.052, 1258.052, 1258.052, 1635.3, 1258.052, 1258.052, 1635.3, 1258.052, 1635.3, 1635.3]
Total no. of outliers in column 142 are 4 Outlier Value are [52.34, 35.68, 22.13, 103.39]
Total no. of outliers in column 150 are 23 Outlier Value are [19.388, 20.684, 20.298, 17.233, 18.857, 18.415, 17.579, 17.633, 22.318, 18.473, 19.781, 16.671, 20.111, 16.737000000000002, 17.899, 19.822, 17.406, 17.747, 18.736, 17.297, 19.556, 16.564, 16.852999999999998]
Total no. of outliers in column 151 are 12 Outlier Value are [140.243, 459.929, 536.564, 271.726, 229.849, 403.73800000000006, 357.49, 109.812, 182.901, 515.283, 290.535, 462.171]
Total no. of outliers in column 155 are 18 Outlier Value are [12.71, 12.61, 12.61, 7.732, 7.732, 7.732, 7.732, 7.732, 7.732, 11.492, 11.492, 11.492, 11.492, 3.94, 11.492, 11.492, 11.492, 11.492]
Total no. of outliers in column 159 are 35 Outlier Value are [5781.0, 5655.0, 5726.0, 5803.0, 5665.0, 5366.0, 5913.0, 5575.0, 5448.0, 7569.0, 7600.0, 7653.0, 5391.0, 7657.0, 5475.0, 7709.0, 5477.0, 5684.0, 7791.0, 5703.0, 7716.0, 5480.0, 6011.0, 7669.0, 7673.0, 5921.0, 7708.0, 5978.0, 5923.0, 7599.0, 5704.0, 5081.0, 5054.0, 3951.0, 4361.0]
Total no. of outliers in column 160 are 54 Outlier Value are [2900.0, 2942.0, 2478.0, 3530.0, 3623.0, 3130.0, 3301.0, 3511.0, 2841.0, 4170.0, 3309.0, 3290.0, 3417.0, 3294.0, 3278.0, 2615.0, 3253.0, 3184.0, 3287.0, 3775.0, 3267.0, 3454.0, 3046.0, 3218.0, 2426.0, 3295.0, 2947.0, 2844.0, 3235.0, 2940.0, 3363.0, 2438.0, 3132.0, 3635.0, 3785.0, 3418.0, 3190.0, 3645.0, 3803.0, 3936.0, 3727.0, 3472.0, 3205.0, 2446.0, 3362.0, 3290.0, 2411.0, 3421.0, 3521.0, 3280.0, 3892.0, 3710.0, 3608.0, 4099.0]
Total no. of outliers in column 161 are 31 Outlier Value are [19038.0, 28188.0, 20118.0, 21777.0, 18949.0, 17801.0, 18389.0, 20499.0, 22267.0, 18587.0, 19941.0, 17918.0, 18140.0, 28637.0, 19740.0, 18313.0, 20802.0, 21304.0, 37943.0, 19497.0, 20032.0, 16819.0, 17359.0, 22817.0, 19581.0, 25359.0, 20488.0, 19368.0, 22504.0, 18540.0, 18365.0]
Total no. of outliers in column 162 are 30 Outlier Value are [31669.0, 28916.0, 27194.0, 27854.0, 27277.0, 36871.0, 27784.0, 24439.0, 36664.0, 27513.0, 34717.0, 25128.0, 24861.0, 27425.0, 27952.0, 25746.0, 30363.0, 25350.0, 31318.0, 28863.0, 25846.0, 24486.0, 24697.0, 35503.0, 29509.0, 24902.0, 25794.0, 24464.0, 30212.0, 26140.0]
Total no. of outliers in column 163 are 32 Outlier Value are [0.912, 0.921, 0.931, 0.942, 0.915, 0.89, 0.95, 0.918, 0.8959999999999999, 0.926, 0.901, 0.947, 0.8909999999999999, 0.906, 0.901, 0.894, 0.899, 0.913, 0.899, 0.957, 0.954, 0.895, 0.947, 0.901, 0.8759999999999999, 0.949, 0.875, 0.944, 0.926, 0.945, 0.935, 0.8490000000000001]
Total no. of outliers in column 166 are 8 Outlier Value are [10.3, 21.1, 7.0, 9.3, 18.9, 6.4, 7.3, 7.2]
Total no. of outliers in column 167 are 3 Outlier Value are [8.4, 8.8, 16.3]
Total no. of outliers in column 169 are 4 Outlier Value are [1.0, 1.143, 1.042, 0.994]
Total no. of outliers in column 170 are 0 Outlier Value are []
Total no. of outliers in column 175 are 6 Outlier Value are [1.172, 1.1437, 1.1437, 1.1437, 1.1437, 1.1437]
Total no. of outliers in column 177 are 6 Outlier Value are [1.858, 1.858, 1.858, 1.858, 1.858, 1.858]
Total no. of outliers in column 180 are 6 Outlier Value are [30.52, 48.67, 29.08, 31.71, 33.78, 31.87]
Total no. of outliers in column 181 are 5 Outlier Value are [1.276, 1.242, 1.3130000000000002, 3.573, 1.315]
Total no. of outliers in column 182 are 4 Outlier Value are [53.98, 34.84, 55.0, 39.41]
Total no. of outliers in column 183 are 9 Outlier Value are [5.733, 5.476, 5.0139999999999985, 6.093, 5.381, 6.119, 62.274, 72.947, 56.49]
Total no. of outliers in column 184 are 6 Outlier Value are [2.1552, 0.6874, 0.5184, 0.5828, 0.523, 3.2283]
Total no. of outliers in column 185 are 3 Outlier Value are [30.22, 267.91, 54.09]
Total no. of outliers in column 188 are 3 Outlier Value are [111.677, 191.83, 116.057]
Total no. of outliers in column 195 are 11 Outlier Value are [4.617, 4.838, 4.838, 4.838, 4.838, 4.838, 4.838, 4.838, 4.838, 4.619, 4.619]
Total no. of outliers in column 198 are 8 Outlier Value are [10.017, 6.541, 4.985, 8.898, 2.756, 8.206, 3.508, 4.424]
Total no. of outliers in column 200 are 4 Outlier Value are [199.62, 156.08, 94.32, 183.66]
Total no. of outliers in column 201 are 10 Outlier Value are [25.06, 54.26, 23.37, 24.06, 23.55, 24.76, 27.52, 58.46, 32.84, 126.53]
Total no. of outliers in column 208 are 7 Outlier Value are [172.34900000000005, 170.36, 165.553, 163.121, 165.497, 164.549, 160.234]
Total no. of outliers in column 218 are 10 Outlier Value are [7.5616, 7.3083, 7.9727, 7.5869, 8.8015, 7.8715, 7.5756, 8.1543, 7.9262, 7.569]
Total no. of outliers in column 223 are 1 Outlier Value are [1768.8802]
Total no. of outliers in column 225 are 18 Outlier Value are [2492.7998, 2887.2002, 2448.5996, 2458.2998, 2470.9995, 2568.8003, 2885.7998, 2628.2002, 2803.1001, 2523.4995, 2427.0, 2388.6001, 2518.3999, 2368.3999, 2466.2002, 3601.2998, 2571.6997, 2600.5996]
Total no. of outliers in column 250 are 6 Outlier Value are [295.763, 279.0726, 346.7927, 327.6722, 943.8936, 1119.7042]
Total no. of outliers in column 255 are 4 Outlier Value are [0.8194, 0.7803, 0.8278, 0.9255]
Total no. of outliers in column 268 are 0 Outlier Value are []
Total no. of outliers in column 269 are 17 Outlier Value are [10.1529, 10.1529, 7.3173, 7.3173, 7.3173, 7.3173, 7.3173, 7.3173, 8.478, 8.478, 8.478, 8.0099, 8.0099, 8.0099, 8.0099, 8.0099, 8.0099]
Total no. of outliers in column 418 are 0 Outlier Value are []
Total no. of outliers in column 419 are 0 Outlier Value are []
Total no. of outliers in column 423 are 21 Outlier Value are [213.0178, 178.7377, 424.2152, 295.525, 210.1032, 196.135, 193.2378, 218.8146, 204.6813, 223.8064, 196.3084, 202.5139, 187.1087, 178.7205, 184.1857, 180.7213, 194.763, 212.007, 226.8931, 176.7227, 201.19]
Total no. of outliers in column 432 are 35 Outlier Value are [582.364, 678.8889, 511.4653, 643.2664, 867.4989, 495.2039, 678.7382, 601.5305, 760.1053, 773.1043, 776.7051, 586.0527, 853.9353, 513.6025, 838.0066, 579.2769999999998, 607.3661, 533.9541, 994.2857, 963.8833, 538.7728, 837.3452, 952.1274, 591.7942, 606.5387, 602.8344, 764.1426, 907.6215, 585.7501, 662.1165, 590.8127, 913.6022, 851.7639, 480.6764, 791.3774]
Total no. of outliers in column 433 are 30 Outlier Value are [930.9577, 907.5269, 935.5316, 908.0021, 887.0027, 914.7125, 955.286, 970.1357, 902.7436, 890.3646, 895.5665, 993.0314, 968.6747, 941.4168, 977.1353, 901.1494, 936.9642, 917.4825, 920.5273, 884.6673, 905.148, 945.6947, 911.0764, 953.0201, 960.2559, 884.0306, 900.7229, 945.5587, 947.3393, 995.7447]
Total no. of outliers in column 438 are 7 Outlier Value are [239.0625, 444.7059, 260.8696, 851.6129, 185.7143, 353.1646, 239.3617]
Total no. of outliers in column 439 are 7 Outlier Value are [218.0073, 241.791, 455.7545, 549.4949, 647.8827, 254.1985, 657.7621]
Total no. of outliers in column 460 are 19 Outlier Value are [78.3719, 75.0239, 70.2619, 118.7998, 73.7347, 149.3851, 70.684, 83.6569, 72.885, 72.0924, 76.8379, 71.182, 72.8696, 72.0207, 133.9113, 70.6707, 73.4011, 73.4752, 75.3229]
Total no. of outliers in column 468 are 23 Outlier Value are [995.2807, 957.554, 983.4625, 967.2532, 945.6885, 934.2229, 991.7375, 932.737, 943.7152, 943.8219, 966.1049, 946.6191, 964.5691, 969.917, 946.0468, 999.877, 968.5841, 944.0, 953.7056, 930.7692, 941.678, 938.8258, 987.5866]
Total no. of outliers in column 472 are 10 Outlier Value are [285.9, 292.8947, 312.5152, 365.3171, 285.9717, 315.7949, 289.3312, 416.257, 492.7718, 303.237]
Total no. of outliers in column 474 are 16 Outlier Value are [211.5006, 354.4169, 189.7559, 227.8544, 131.6493, 113.124, 126.7858, 122.2429, 120.6696, 253.3888, 196.7546, 117.428, 131.9725, 120.0646, 415.4355, 134.2458]
Total no. of outliers in column 476 are 17 Outlier Value are [71.7657, 157.1088, 65.9965, 79.3758, 79.8723, 137.4644, 274.8871, 70.829, 65.6944, 73.4527, 88.9624, 72.2292, 157.10299999999995, 75.368, 70.0774, 78.5433, 79.0222]
Total no. of outliers in column 482 are 0 Outlier Value are []
Total no. of outliers in column 483 are 39 Outlier Value are [989.4737, 820.7547, 839.5604, 905.4152, 843.7811, 948.0, 821.8487, 811.2676, 813.5922, 869.5652, 844.8, 923.2877, 983.691, 886.0987, 905.8065, 792.2581, 986.4253, 846.4646, 893.7063, 804.2781, 800.0, 837.1795, 961.7021, 964.1026, 852.3161, 831.6547, 832.2034, 842.0091, 822.9885, 959.5238, 792.8571, 907.4286, 804.8583, 852.7027, 858.8832, 791.3357, 887.0968, 948.0519, 925.1799]
Total no. of outliers in column 484 are 33 Outlier Value are [979.8817, 981.4324, 887.8613, 950.3546, 934.2282, 911.332, 893.5167, 927.5362, 963.8095, 955.8824, 980.0643, 885.4271, 976.4103, 901.5385, 902.8571, 951.6484, 858.1197, 849.6124, 922.0104, 985.6, 964.7059, 879.7654, 938.8128, 881.3559, 996.8586, 929.4118, 910.4294, 876.5217, 923.1504, 957.1429, 850.6757, 965.4135, 928.3019]
Total no. of outliers in column 485 are 30 Outlier Value are [892.1933, 869.3446, 866.3636, 865.2174, 871.6981, 984.6154, 945.9318, 930.504, 990.303, 963.2, 956.0606, 871.4286, 896.063, 994.0, 873.6, 901.8868, 882.6255, 956.4246, 883.0601, 991.6667, 883.2714, 876.2887, 957.7982, 960.8392, 955.6364, 853.7313, 902.8571, 909.7436, 891.1854, 856.6372]
Total no. of outliers in column 486 are 0 Outlier Value are []
Total no. of outliers in column 487 are 0 Outlier Value are []
Total no. of outliers in column 488 are 0 Outlier Value are []
Total no. of outliers in column 489 are 11 Outlier Value are [972.0102, 963.5783, 985.2217, 980.6452, 953.5211, 994.0035, 958.9577, 987.5458, 950.7692, 981.3953, 972.9915]
Total no. of outliers in column 499 are 0 Outlier Value are []
Total no. of outliers in column 500 are 0 Outlier Value are []
Total no. of outliers in column 510 are 32 Outlier Value are [240.7767, 263.4538, 349.2063, 172.0755, 200.7117, 218.1818, 174.8988, 301.4493, 243.4783, 289.6552, 205.5866, 252.6316, 202.7397, 171.1628, 175.3425, 400.0, 439.2157, 184.3478, 247.343, 174.8072, 253.7931, 451.4851, 249.711, 217.1429, 196.0199, 200.823, 206.2222, 204.5307, 221.6216, 210.596, 174.0988, 244.2748]
Total no. of outliers in column 511 are 0 Outlier Value are []
Total no. of outliers in column 521 are 20 Outlier Value are [1000.0, 907.91, 1000.0, 1000.0, 776.2169, 1000.0, 1000.0, 604.2009, 1000.0, 718.6039999999998, 1000.0, 553.2097, 1000.0, 474.6376, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0]
Total no. of outliers in column 546 are 21 Outlier Value are [2.1716, 2.3393, 2.1408, 2.3906, 2.1487, 2.1536, 2.8654, 3.9786, 2.1764, 2.1878, 2.2472, 2.5242, 2.3764, 2.0985, 2.116, 3.2092, 2.2021, 2.1592, 3.2310000000000003, 3.0437, 2.7059]
Total no. of outliers in column 547 are 15 Outlier Value are [372.822, 420.71, 389.206, 421.702, 388.906, 387.356, 387.398, 417.628, 388.152, 383.906, 383.352, 382.534, 389.53, 388.628, 384.568]
Total no. of outliers in column 548 are 0 Outlier Value are []
Total no. of outliers in column 549 are 34 Outlier Value are [6.2712, 2.6651, 2.722, 4.7366, 4.8467, 3.4902, 3.0519, 3.0544, 4.5986, 3.0588, 3.2976, 2.5351, 3.2604, 4.2729, 2.7105, 3.0304, 2.4955, 2.6488, 3.1688, 3.9653, 2.6285, 3.0839, 5.5088, 3.1059, 2.7536, 7.0656, 2.6010000000000004, 3.4486, 2.8960000000000004, 5.1232, 6.3371, 3.9115, 3.2403, 3.3495]
Total no. of outliers in column 550 are 2 Outlier Value are [131.68, 90.7]
Total no. of outliers in column 551 are 4 Outlier Value are [39.33, 25.47, 11.42, 6.84]
Total no. of outliers in column 559 are 11 Outlier Value are [0.9412, 0.9412, 0.9412, 0.9412, 1.0737, 0.9412, 0.9412, 0.9511, 1.0099, 0.9511, 0.98]
Total no. of outliers in column 562 are 10 Outlier Value are [311.404, 298.664, 299.876, 299.62, 284.8540000000001, 284.922, 286.65, 283.92, 284.362, 288.8640000000001]
Total no. of outliers in column 563 are 12 Outlier Value are [1.0247, 1.0256, 1.1978, 1.0607, 1.1116, 1.0394, 1.0189, 1.0799, 0.3049, 1.0103, 1.2988, 1.2254]
Total no. of outliers in column 564 are 18 Outlier Value are [15.07, 13.93, 32.58, 14.51, 13.61, 19.61, 13.92, 14.76, 13.69, 13.57, 14.76, 13.55, 16.06, 14.49, 15.04, 14.13, 14.26, 16.54]
Total no. of outliers in column 570 are 18 Outlier Value are [466.5409, 367.44, 391.5764, 401.4482, 364.1436, 331.9618, 317.1964, 379.0045, 360.7645, 336.6718, 410.2409, 365.3818, 384.6536, 335.5982, 328.4664, 586.9145, 589.5082, 458.8464]
Total no. of outliers in column 571 are 32 Outlier Value are [1.0912, 1.0912, 1.0912, 1.0912, 1.0744, 1.0912, 1.0912, 1.0912, 1.0024, 1.0912, 1.0744, 1.0936, 1.0744, 1.0912, 0.9802, 1.0912, 1.0744, 1.1073, 1.0744, 0.9847, 0.9847, 0.9847, 0.9847, 1.109, 0.9847, 1.1073, 0.9847, 1.0744, 0.9847, 0.9847, 1.0744, 0.9847]
Total no. of outliers in column 572 are 60 Outlier Value are [439.05, 439.05, 439.05, 439.05, 420.24, 439.05, 434.04, 454.56, 426.69, 445.32, 451.69, 426.85, 452.54, 435.57, 435.26, 438.87, 438.87, 438.87, 438.87, 438.87, 438.87, 438.87, 438.87, 438.87, 436.52, 436.52, 438.87, 436.52, 436.52, 436.52, 438.87, 415.03, 430.37, 445.8, 427.42, 445.8, 431.71, 431.71, 445.8, 427.42, 431.71, 432.94, 431.71, 445.8, 431.71, 427.42, 431.71, 431.71, 431.71, 431.71, 431.71, 427.42, 427.42, 431.71, 441.92, 441.92, 441.92, 441.92, 439.29, 441.92]
Total no. of outliers in column 573 are 60 Outlier Value are [1.2098, 1.2098, 1.2098, 1.2098, 1.4316, 1.2098, 1.1022, 1.6228, 1.4024, 1.3634, 1.2515, 1.1093, 1.1669, 1.138, 1.138, 1.138, 1.138, 1.3206, 1.3483, 1.138, 1.2877, 1.138, 1.138, 1.138, 2.1967, 2.1967, 2.1967, 2.1967, 2.1967, 2.1967, 2.1967, 2.1967, 2.1967, 1.1022, 1.1022, 2.1967, 1.1022, 1.1022, 1.1022, 2.1967, 1.2041, 1.2041, 1.2571, 1.2571, 1.2041, 1.2571, 1.2571, 1.2041, 1.2571, 1.2571, 1.2571, 1.2571, 1.2571, 1.2571, 1.2571, 1.2113, 1.2113, 1.2113, 1.2113, 1.2113]
Total no. of outliers in column 589 are 46 Outlier Value are [397.5003, 397.5003, 397.5003, 397.5003, 397.5003, 382.6619, 510.041, 706.824, 706.824, 388.9648, 388.9648, 388.9648, 388.9648, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 474.0812, 390.4146, 390.4146, 390.4146, 390.4146, 390.4146, 390.4146, 737.3048, 737.3048, 427.4732, 545.6838, 545.6838, 545.6838, 545.6838, 579.1817, 579.1817, 579.1817, 579.1817, 414.4256, 402.6874]
Total no. of outliers in column Pass/Fail are 104 Outlier Value are [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
print('''\n\033[1m''' + '''Univariate Analysis of each columns:''' + '''\033[0m''')
print('''\n\033[1m''' + '''Checking of skewness and distribution of each columns:''' + '''\033[0m''')
for x in sd1:
series = sd1[x]
skewness = series.skew()
if skewness > -.5 and skewness < .5 :
print(x,"is Symmetrically Skewed as Skewness =",round(skewness,3),'\n')
elif skewness > .25:
print(x,"is Negatively Skewed towards Left side of asymmetric distribution as Skewness =",round(skewness,3),'\n')
elif skewness < -.25:
print(x,"is Positively Skewed towards Right side of asymmetric distribution as Skewness =",round(skewness,3),'\n')
sns.distplot(sd1[x])
plt.xlabel(x)
plt.show()
Univariate Analysis of each columns: Checking of skewness and distribution of each columns: 0 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.515
1 is Symmetrically Skewed as Skewness = -0.044
2 is Symmetrically Skewed as Skewness = -0.308
3 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.724
4 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 19.734
6 is Symmetrically Skewed as Skewness = -0.109
12 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 7.21
14 is Symmetrically Skewed as Skewness = 0.17
15 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 17.077
16 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 37.07
18 is Symmetrically Skewed as Skewness = 0.196
19 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -9.863
21 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 2.375
22 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -2.182
23 is Symmetrically Skewed as Skewness = 0.357
24 is Symmetrically Skewed as Skewness = -0.054
25 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -2.36
28 is Symmetrically Skewed as Skewness = -0.063
29 is Symmetrically Skewed as Skewness = -0.302
31 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.16
32 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 6.329
33 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 9.127
34 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 6.418
35 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 8.15
36 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -6.418
37 is Symmetrically Skewed as Skewness = 0.166
38 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.016
39 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 4.658
40 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -1.893
41 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 12.309
43 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.555
44 is Symmetrically Skewed as Skewness = -0.192
45 is Symmetrically Skewed as Skewness = 0.375
47 is Symmetrically Skewed as Skewness = -0.375
48 is Symmetrically Skewed as Skewness = -0.02
51 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -0.51
55 is Symmetrically Skewed as Skewness = 0.37
59 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 4.731
61 is Symmetrically Skewed as Skewness = -0.247
62 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 5.308
63 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 10.707
64 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.132
67 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 20.825
68 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -2.903
71 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.255
74 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 39.585
83 is Symmetrically Skewed as Skewness = -0.08
88 is Symmetrically Skewed as Skewness = -0.273
90 is Symmetrically Skewed as Skewness = 0.348
96 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.266
115 is Symmetrically Skewed as Skewness = -0.272
117 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 38.006
120 is Symmetrically Skewed as Skewness = 0.352
122 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.72
125 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.962
126 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.722
128 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -5.379
129 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -0.984
133 is Symmetrically Skewed as Skewness = -0.278
134 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.829
135 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 6.193
136 is Symmetrically Skewed as Skewness = 0.334
137 is Symmetrically Skewed as Skewness = 0.385
138 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.827
139 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 2.09
142 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 15.04
150 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.298
151 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 13.006
155 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 8.539
159 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 4.207
160 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 3.996
161 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 2.232
162 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.831
163 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 5.265
166 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 5.519
167 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 10.873
169 is Symmetrically Skewed as Skewness = 0.208
170 is Symmetrically Skewed as Skewness = -0.004
175 is Symmetrically Skewed as Skewness = 0.487
177 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.835
180 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.777
181 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.931
182 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 2.145
183 is Symmetrically Skewed as Skewness = 0.202
184 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 18.175
185 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 30.709
188 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.766
195 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 10.431
198 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 9.547
200 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 13.44
201 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 9.747
208 is Symmetrically Skewed as Skewness = 0.163
218 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.682
223 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 17.146
225 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.107
250 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 6.752
255 is Symmetrically Skewed as Skewness = 0.21
268 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.582
269 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.836
418 is Symmetrically Skewed as Skewness = 0.457
419 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.5
423 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 2.086
432 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 3.347
433 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.365
438 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 10.165
439 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 6.757
460 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.868
468 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.266
472 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.575
474 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 5.468
476 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 5.459
482 is Symmetrically Skewed as Skewness = 0.474
483 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.724
484 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.545
485 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.536
486 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.623
487 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.175
488 is Symmetrically Skewed as Skewness = 0.357
489 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.056
499 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.745
500 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.921
510 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 4.11
511 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.702
521 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 9.04
546 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.722
547 is Symmetrically Skewed as Skewness = -0.406
548 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.121
549 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 4.231
550 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 13.112
551 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 23.585
559 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 0.851
562 is Symmetrically Skewed as Skewness = 0.001
563 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 1.059
564 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 2.013
570 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -8.659
571 is Positively Skewed towards Right side of asymmetric distribution as Skewness = -1.276
572 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 4.299
573 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 4.359
589 is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 2.716
Pass/Fail is Negatively Skewed towards Left side of asymmetric distribution as Skewness = 3.487
print('''\n\033[1m''' + '''Selecting predictors and target attributes''' + '''\033[0m''')
x = sd1.drop('Pass/Fail',axis=1)
y=sd1['Pass/Fail']
accuracies={}
Selecting predictors and target attributes
print('''\n\033[1m''' + '''Spliting in Train and Test sets''' + '''\033[0m''')
x_train, x_test, y_train, y_test = train_test_split(x, y,train_size=0.7, test_size=0.3,random_state=202)
print('X_Train',len(x_train),'X_Test',len(x_test))
print('Y_Train',len(y_train),'Y_Test',len(y_test))
Spliting in Train and Test sets
X_Train 1096 X_Test 471
Y_Train 1096 Y_Test 471
print('''\n\033[1m''' + '''Check for target balancing''' + '''\033[0m''')
y.value_counts()
Check for target balancing
-1.0 1463 1.0 104 Name: Pass/Fail, dtype: int64
print('''\n\033[1m''' + '''Check for target balancing''' + '''\033[0m''')
count_Pass = len(sd[sd['Pass/Fail'] == -1])
print('Total Count of Pass(-1) yield for in-house line testing:',count_Pass)
count_Fail = len(y[y==1])
print('Total Count of Fail(1) yield for in-house line testing:',count_Fail)
Pass= count_Pass/(count_Pass+count_Fail)
print("Percentage of Pass(-1) yield for in-house line testing:",Pass*100)
Fail = count_Fail/(count_Pass+count_Fail)
print("Percentage of Fail(1) yield for in-house line testing:",Fail*100)
Check for target balancing
Total Count of Pass(-1) yield for in-house line testing: 1463
Total Count of Fail(1) yield for in-house line testing: 104
Percentage of Pass(-1) yield for in-house line testing: 93.36311423101468
Percentage of Fail(1) yield for in-house line testing: 6.636885768985322
print('''\n\033[1m''' + '''COUNT PLOT OF TARGET COLUMN''' + '''\033[0m''')
sns.countplot(x='Pass/Fail',data=sd)
COUNT PLOT OF TARGET COLUMN
<AxesSubplot:xlabel='Pass/Fail', ylabel='count'>
mx=max(count_Pass,count_Fail)
mn=min(count_Pass,count_Fail)
if mn/mx >= .67:
print('Target column is balanced')
else:
print('Taarget column is Imbalanced')
Taarget column is Imbalanced
print('''\n\033[1m''' + '''RANDOM OVER-SAMPLING''' + '''\033[0m''')
from imblearn.over_sampling import RandomOverSampler
bal = RandomOverSampler()
x_bal, y_bal = bal.fit_sample(x, y)
rn=x_bal.shape[0] - x.shape[0]
print('New random picked points ',rn)
y_bal.value_counts().plot(kind='bar', title='Count')
RANDOM OVER-SAMPLING
New random picked points 1359
<AxesSubplot:title={'center':'Count'}>
nsd = pd.concat([x_bal,y_bal], axis=1)
print('''\n\033[1m''' + '''NEW DATASET WITH BALANCED TARGET COLUMN''' + '''\033[0m''')
nsd
NEW DATASET WITH BALANCED TARGET COLUMN
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | 18 | 19 | 21 | 22 | 23 | 24 | 25 | 28 | 29 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 43 | 44 | 45 | 47 | 48 | 51 | 55 | 59 | 61 | 62 | 63 | 64 | 67 | 68 | 71 | 74 | 83 | 88 | 90 | 96 | 115 | 117 | 120 | 122 | 125 | 126 | 128 | 129 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 142 | 150 | 151 | 155 | 159 | 160 | 161 | 162 | 163 | 166 | 167 | 169 | 170 | 175 | 177 | 180 | 181 | 182 | 183 | 184 | 185 | 188 | 195 | 198 | 200 | 201 | 208 | 218 | 223 | 225 | 250 | 255 | 268 | 269 | 418 | 419 | 423 | 432 | 433 | 438 | 439 | 460 | 468 | 472 | 474 | 476 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 499 | 500 | 510 | 511 | 521 | 546 | 547 | 548 | 549 | 550 | 551 | 559 | 562 | 563 | 564 | 570 | 571 | 572 | 573 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3030.93 | 2564.00 | 2187.7333 | 1411.1265 | 1.3602 | 97.6133 | 202.4396 | 7.9558 | 414.8710 | 10.0433 | 192.3963 | 12.5190 | -5419.00 | 2916.50 | -4043.75 | 751.0000 | 0.8955 | 64.2333 | 2.0222 | 3.5191 | 83.3971 | 9.5126 | 50.6170 | 64.2588 | 49.3830 | 66.3141 | 86.9555 | 117.5132 | 61.29 | 4.515 | 352.7173 | 10.1841 | 130.3691 | 1.3072 | 141.2282 | 218.3174 | 2834.0 | -1.7264 | 10.6231 | 108.6427 | 16.1445 | 21.7264 | 0.9226 | 148.6009 | 84.0793 | 0.0 | 7.2163 | 1747.6049 | 8671.9301 | -0.2786 | 748.6115 | 58.4306 | 6.3788 | 2.639 | 0.8656 | 3.353 | 3.188 | -0.0473 | 1000.7263 | 39.2373 | 123.0 | 111.3 | 75.2 | 46.2000 | 350.6710 | 6.78 | 4.271 | 10.284 | 0.41 | 1017.0 | 967.0 | 1066.0 | 368.0 | 0.090 | 2.0 | 0.9 | 0.046 | 0.7250 | 0.9499 | 0.160 | 20.95 | 0.333 | 12.49 | 16.713 | 0.0803 | 5.72 | 65.363 | 0.292 | 0.296 | 10.30 | 5.38 | 97.314 | 3.4789 | 175.2173 | 1940.3994 | 219.9453 | 0.4269 | 40.855 | 4.5152 | 525.0965 | 0.0000 | 53.6840 | 26.3617 | 49.0013 | 44.5055 | 42.2737 | 29.9394 | 311.6377 | 63.7987 | 13.6778 | 31.9893 | 613.3069 | 291.4842 | 494.6996 | 178.1759 | 843.1138 | 0.0000 | 53.1098 | 0.0000 | 0.0000 | 0.0000 | 64.6707 | 0.0000 | 0.0 | 1.0616 | 395.570 | 75.752 | 0.4234 | 12.93 | 0.78 | 0.4385 | 264.272 | 0.6510 | 5.16 | 533.8500 | 2.1113 | 8.95 | 0.3157 | 71.9005 | -1.0 |
| 1 | 3095.78 | 2465.14 | 2230.4222 | 1463.6606 | 0.8294 | 102.3433 | 200.5470 | 10.1548 | 414.7347 | 9.2599 | 191.2872 | 12.4608 | -5441.50 | 2604.25 | -3498.75 | -1640.2500 | 1.2973 | 68.4222 | 2.2667 | 3.4171 | 84.9052 | 9.7997 | 50.6596 | 64.2828 | 49.3404 | 64.9193 | 87.5241 | 118.1188 | 78.25 | 2.773 | 352.2445 | 10.0373 | 133.1727 | 1.2887 | 145.8445 | 205.1695 | 2853.0 | 0.8073 | 10.3092 | 113.9800 | 10.9036 | 19.1927 | 1.1598 | 154.3709 | 82.3494 | 0.0 | 6.8043 | 1931.6464 | 8407.0299 | 0.5854 | 731.2517 | 58.6680 | 6.5061 | 2.541 | 0.8703 | 2.771 | 3.272 | -0.0946 | 998.1081 | 37.9213 | 98.0 | 80.3 | 81.0 | 56.2000 | 219.7679 | 5.70 | 6.285 | 13.077 | 0.35 | 568.0 | 59.0 | 297.0 | 3277.0 | 0.112 | 2.2 | 1.1 | 0.561 | 1.0498 | 1.0181 | 0.325 | 17.99 | 0.439 | 10.14 | 16.358 | 0.0892 | 6.92 | 82.986 | 0.222 | 0.316 | 8.02 | 3.74 | 134.250 | 3.9578 | 128.4285 | 1988.0000 | 193.0287 | 0.5749 | 29.743 | 3.6327 | 0.0000 | 368.9713 | 61.8918 | 8.4887 | 199.7866 | 48.5294 | 37.5793 | 40.4475 | 463.2883 | 73.5536 | 13.2430 | 30.8643 | 0.0000 | 246.7762 | 0.0000 | 359.0444 | 130.6350 | 820.7900 | 194.4371 | 0.0000 | 0.0000 | 0.0000 | 141.4365 | 0.0000 | 0.0 | 1.3526 | 408.798 | 74.640 | 0.7193 | 16.00 | 1.33 | 0.1745 | 264.272 | 0.6510 | 5.16 | 535.0164 | 2.4335 | 5.92 | 0.2653 | 208.2045 | -1.0 |
| 2 | 2932.61 | 2559.94 | 2186.4111 | 1698.0172 | 1.5102 | 95.4878 | 202.0179 | 9.5157 | 416.7075 | 9.3144 | 192.7035 | 12.5404 | -5447.75 | 2701.75 | -4047.00 | -1916.5000 | 1.3122 | 67.1333 | 2.3333 | 3.5986 | 84.7569 | 8.6590 | 50.1530 | 64.1114 | 49.8470 | 65.8389 | 84.7327 | 118.6128 | 14.37 | 5.434 | 364.3782 | 9.8783 | 131.8027 | 1.2992 | 141.0845 | 185.7574 | 2936.0 | 23.8245 | 10.1685 | 115.6273 | 11.3019 | 16.1755 | 0.8694 | 145.8000 | 84.7681 | 0.0 | 7.1041 | 1685.8514 | 9317.1698 | -0.1343 | 718.5777 | 58.4808 | 6.4527 | 2.882 | 0.8798 | 3.094 | 3.272 | -0.1892 | 998.4440 | 42.0579 | 89.0 | 126.4 | 96.5 | 45.1001 | 306.0380 | 8.33 | 4.819 | 8.443 | 0.47 | 562.0 | 788.0 | 759.0 | 2100.0 | 0.187 | 2.1 | 1.4 | 0.319 | 1.0824 | 0.9677 | 0.326 | 17.78 | 0.745 | 13.31 | 22.912 | 0.1959 | 9.21 | 60.110 | 0.139 | 0.949 | 16.73 | 5.09 | 79.618 | 2.4266 | 182.4956 | 839.6006 | 104.4042 | 0.4166 | 29.621 | 3.9133 | 0.0000 | 0.0000 | 50.6425 | 18.7546 | 109.5747 | 60.0000 | 70.9161 | 32.3594 | 21.3645 | 148.0287 | 45.5423 | 13.3923 | 434.2674 | 151.7665 | 0.0000 | 190.3869 | 746.9150 | 74.0741 | 191.7582 | 250.1742 | 0.0000 | 0.0000 | 240.7767 | 244.2748 | 0.0 | 0.7942 | 411.136 | 74.654 | 0.1832 | 16.16 | 0.85 | 0.3718 | 267.064 | 0.9032 | 1.10 | 535.0245 | 2.0293 | 11.21 | 0.1882 | 82.8602 | 1.0 |
| 3 | 2988.72 | 2479.90 | 2199.0333 | 909.7926 | 1.3204 | 104.2367 | 201.8482 | 9.6052 | 422.2894 | 9.6924 | 192.1557 | 12.4782 | -5468.25 | 2648.25 | -4515.00 | -1657.2500 | 1.3137 | 62.9333 | 2.6444 | 3.3813 | 84.9105 | 8.6789 | 50.5100 | 64.1125 | 49.4900 | 65.1951 | 86.6867 | 117.0442 | 76.90 | 1.279 | 363.0273 | 9.9305 | 131.8027 | 1.3027 | 142.5427 | 189.9079 | 2936.0 | 24.3791 | 10.2112 | 116.1818 | 13.5597 | 15.6209 | 0.9761 | 147.6545 | 70.2289 | 0.0 | 7.5925 | 1752.0968 | 8205.7000 | 0.0411 | 709.0867 | 58.6635 | 6.4935 | 3.132 | 1.3660 | 2.480 | 3.119 | 0.2838 | 980.4510 | 41.1025 | 127.0 | 118.0 | 123.7 | 47.8000 | 162.4320 | 5.51 | 9.073 | 15.241 | 0.35 | 859.0 | 355.0 | 3433.0 | 3004.0 | 0.068 | 1.7 | 0.9 | 0.241 | 0.9386 | 0.8567 | 0.390 | 16.22 | 0.693 | 14.67 | 22.562 | 0.1786 | 5.69 | 52.571 | 0.139 | 1.264 | 13.56 | 5.92 | 104.950 | 5.5398 | 152.0885 | 820.3999 | 94.0954 | 0.4212 | 31.830 | 3.1959 | 317.7362 | 0.0000 | 94.4594 | 76.0354 | 181.2641 | 34.0336 | 41.5236 | 27.6824 | 24.2831 | 100.0021 | 48.4887 | 35.4323 | 225.0169 | 100.4883 | 305.7500 | 88.5553 | 104.6660 | 71.7583 | 0.0000 | 336.7660 | 0.0000 | 711.6418 | 113.5593 | 0.0000 | 0.0 | 1.1650 | 372.822 | 72.442 | 1.8804 | 131.68 | 39.33 | 0.7288 | 268.228 | 0.6511 | 7.32 | 530.5682 | 2.0253 | 9.33 | 0.1738 | 73.8432 | -1.0 |
| 4 | 3032.24 | 2502.87 | 2233.3667 | 1326.5200 | 1.5334 | 100.3967 | 201.9424 | 10.5661 | 420.5925 | 10.3387 | 191.6037 | 12.4735 | -5476.25 | 2635.25 | -3987.50 | 117.0000 | 1.2887 | 62.8333 | 3.1556 | 3.2728 | 86.3269 | 8.7677 | 50.2480 | 64.1511 | 49.7520 | 66.1542 | 86.1468 | 121.4364 | 76.39 | 2.209 | 353.3400 | 10.4091 | 176.3136 | 1.0341 | 138.0882 | 233.5491 | 2865.0 | -12.2945 | 9.7948 | 144.0191 | 21.9782 | 32.2945 | 0.9256 | 146.6636 | 65.8417 | 0.0 | 7.5017 | 1828.3846 | 9014.4600 | 0.2189 | 796.5950 | 58.3858 | 6.3551 | 3.148 | 0.9460 | 3.027 | 3.299 | -0.5677 | 993.1274 | 38.1448 | 119.0 | 143.2 | 123.1 | 48.8000 | 296.3030 | 3.64 | 9.005 | 12.506 | 0.43 | 699.0 | 283.0 | 1747.0 | 1443.0 | 0.147 | 3.9 | 0.8 | 0.499 | 0.5760 | 0.8285 | 0.922 | 15.24 | 0.282 | 10.85 | 37.715 | 0.1189 | 3.98 | 72.149 | 0.250 | 0.519 | 19.77 | 5.52 | 92.307 | 4.1338 | 69.1510 | 1406.4004 | 149.2172 | 0.4051 | 19.862 | 3.6163 | 0.0000 | 866.0295 | 85.2255 | 43.8119 | 0.0000 | 25.3521 | 37.4691 | 30.8924 | 44.8980 | 89.9529 | 19.1303 | 42.6838 | 171.4486 | 276.8810 | 461.8619 | 240.1781 | 0.0000 | 587.3773 | 748.1781 | 0.0000 | 293.1396 | 0.0000 | 148.0663 | 0.0000 | 0.0 | 1.4636 | 399.914 | 79.156 | 1.0388 | 19.63 | 1.98 | 0.2156 | 264.272 | 0.6510 | 5.16 | 532.0155 | 2.0275 | 8.83 | 0.2224 | 73.8432 | -1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2921 | 2971.99 | 2502.62 | 2239.3000 | 1192.7495 | 1.2479 | 100.1189 | 201.9597 | 4.7482 | 404.6931 | 9.6496 | 192.3100 | 12.4659 | -5495.75 | 2703.75 | -3914.75 | 1334.5000 | 1.3057 | 62.8333 | 2.7111 | 3.2932 | 83.8746 | 9.2658 | 50.2261 | 64.3328 | 49.7739 | 66.5940 | 88.2109 | 119.1040 | 73.32 | 2.051 | 351.2655 | 10.2185 | 128.3009 | 1.3870 | 146.8727 | 220.7331 | 2859.0 | 12.4309 | 10.8850 | 100.7318 | 22.9100 | 27.5691 | 0.8918 | 155.6164 | 94.8504 | 0.0 | 7.5321 | 1883.8396 | 9054.9200 | 0.0000 | 771.4724 | 57.7826 | 6.5992 | 3.566 | 1.2100 | 2.642 | 3.034 | 0.0000 | 1004.2077 | 41.9975 | 119.0 | 114.5 | 141.8 | 95.4001 | 369.5220 | 6.90 | 6.407 | 18.685 | 0.30 | 424.0 | 424.0 | 1757.0 | 13977.0 | 0.152 | 4.8 | 1.0 | 0.282 | 0.6716 | 0.6378 | 0.966 | 18.43 | 0.201 | 8.67 | 32.633 | 0.1312 | 7.75 | 56.977 | 0.241 | 0.256 | 22.69 | 7.50 | 99.301 | 2.5736 | 171.8003 | 870.8994 | 76.5322 | 0.3216 | 24.105 | 5.0666 | 0.0000 | 636.8627 | 134.9359 | 44.8815 | 0.0000 | 36.8852 | 55.1506 | 25.8126 | 60.3335 | 99.0397 | 25.3071 | 18.9788 | 703.3816 | 74.7126 | 0.0000 | 97.4939 | 208.2840 | 121.8623 | 433.7572 | 128.6364 | 0.0000 | 0.0000 | 68.6627 | 869.5652 | 0.0 | 0.6624 | 400.372 | 80.390 | 0.4503 | 18.25 | 1.46 | 0.3155 | 265.058 | 0.7331 | 4.14 | 531.8100 | 2.2115 | 13.76 | 0.1968 | 15.2909 | 1.0 |
| 2922 | 3037.49 | 2463.11 | 2205.2889 | 1630.3112 | 1.2733 | 98.8056 | 199.7563 | 6.8695 | 396.9662 | 10.3504 | 189.4059 | 12.3741 | -6057.25 | 2609.00 | -4709.25 | -2523.5000 | 1.2825 | 69.0667 | 2.4000 | 3.4074 | 84.3232 | 8.5218 | 50.3583 | 64.2803 | 49.6417 | 66.1861 | 87.0459 | 118.1020 | 78.42 | 3.645 | 355.7327 | 9.7939 | 140.5945 | 1.2920 | 135.7709 | 174.1437 | 2845.0 | 20.8336 | 9.9218 | 121.4282 | 13.8522 | 19.1664 | 0.7465 | 141.6909 | 71.0513 | 0.0 | 6.7860 | 1801.1280 | 8745.8100 | 0.0084 | 746.2591 | 59.6121 | 6.3231 | 2.992 | 1.1870 | 2.813 | 3.345 | 0.5677 | 1012.1633 | 35.3906 | 80.0 | 68.7 | 163.9 | 58.3000 | 468.7301 | 6.16 | 3.482 | 8.915 | 0.57 | 2017.0 | 606.0 | 3360.0 | 6857.0 | 0.228 | 1.3 | 1.4 | 0.446 | 0.8705 | 0.8096 | 0.287 | 18.26 | 0.539 | 9.59 | 32.019 | 0.1709 | 7.57 | 45.544 | 0.269 | 0.999 | 14.54 | 5.18 | 100.343 | 4.1052 | 132.9160 | 1631.0996 | 159.6516 | 0.4905 | 11.820 | 2.8660 | 501.7561 | 0.0000 | 50.6875 | 71.3489 | 271.7258 | 58.3333 | 91.0432 | 26.1531 | 24.8636 | 104.9653 | 33.2962 | 35.3189 | 221.2470 | 282.8157 | 199.0307 | 159.9585 | 185.1347 | 87.0968 | 282.5151 | 148.8698 | 0.0000 | 0.0000 | 98.9474 | 0.0000 | 0.0 | 1.1063 | 409.536 | 77.100 | 0.6548 | 24.00 | 0.57 | 0.1898 | 264.272 | 0.6510 | 5.16 | 533.8327 | 1.5604 | 9.28 | 0.2225 | 30.2219 | 1.0 |
| 2923 | 2977.29 | 2471.50 | 2214.7889 | 1687.4606 | 2.2073 | 97.3378 | 203.7140 | 4.8012 | 407.3098 | 9.0489 | 194.6651 | 12.5590 | -5459.00 | 2635.75 | -3735.00 | -3060.3333 | 1.2913 | 66.3111 | 2.7000 | 3.3181 | 84.4061 | 8.6772 | 50.4095 | 64.2376 | 49.5905 | 66.2913 | 86.2590 | 118.5420 | 79.86 | 2.936 | 361.4473 | 10.0883 | 127.8345 | 1.3679 | 142.7073 | 214.0858 | 2867.0 | 5.6618 | 10.3908 | 113.4964 | 16.4009 | 14.3382 | 0.7986 | 141.0864 | 79.5015 | 0.0 | 7.4968 | 1893.0174 | 8511.4999 | 0.0083 | 731.6244 | 58.7039 | 6.4045 | 3.541 | 0.9649 | 2.884 | 3.293 | 0.0000 | 1012.2367 | 35.3652 | 128.0 | 127.4 | 112.2 | 65.1001 | 584.8420 | 6.14 | 4.233 | 12.612 | 0.22 | 533.0 | 363.0 | 666.0 | 8589.0 | 0.131 | 2.4 | 1.3 | 0.413 | 0.7637 | 0.8015 | 0.447 | 20.64 | 0.341 | 4.43 | 27.913 | 0.1548 | 2.56 | 50.837 | 0.293 | 0.477 | 25.54 | 6.50 | 78.530 | 2.0343 | 157.4490 | 491.0996 | 56.6546 | 0.2832 | 18.020 | 4.4012 | 0.0000 | 0.0000 | 88.1658 | 17.8313 | 280.6557 | 48.1481 | 50.4425 | 23.7461 | 114.8041 | 155.7230 | 41.7914 | 26.6360 | 0.0000 | 528.3820 | 321.3773 | 228.0193 | 464.7093 | 220.2068 | 252.3397 | 887.6457 | 0.0000 | 0.0000 | 35.3266 | 0.0000 | 0.0 | 0.5109 | 405.908 | 74.490 | 0.2556 | 17.28 | 1.00 | 0.2331 | 260.956 | 0.6632 | 7.18 | 531.8455 | 2.2544 | 8.64 | 0.2994 | 90.4575 | 1.0 |
| 2924 | 2964.77 | 2524.44 | 2181.5111 | 1177.0830 | 1.3012 | 100.9333 | 195.5249 | 11.7113 | 408.0994 | 9.8767 | 185.6482 | 12.5098 | -6349.50 | 2966.75 | -4438.25 | 1576.7500 | 0.9853 | 69.0111 | 2.3111 | 4.6423 | 84.8180 | 8.6309 | 50.2512 | 64.2144 | 49.7488 | 65.9697 | 86.7969 | 117.4996 | 78.31 | 2.408 | 358.2536 | 9.8516 | 152.1936 | 1.0790 | 136.8164 | 221.8122 | 2889.0 | -12.1527 | 9.9826 | 120.0409 | 23.1272 | 32.1527 | 0.9728 | 140.6955 | 116.9935 | 0.0 | 7.4108 | 1802.9950 | 9209.4500 | 0.0145 | 786.0266 | 58.9740 | 6.3543 | 3.925 | 1.0970 | 2.685 | 3.161 | -0.0946 | 1012.6356 | 40.0084 | 127.0 | 165.6 | 152.0 | 60.6001 | 377.6400 | 6.18 | 13.812 | 11.782 | 0.41 | 582.0 | 448.0 | 4048.0 | 53.0 | 0.083 | 3.2 | 1.3 | 0.103 | 0.9737 | 0.8317 | 0.372 | 15.39 | 0.338 | 6.38 | 24.483 | 0.0866 | 6.48 | 56.252 | 0.249 | 0.824 | 15.99 | 6.63 | 105.003 | 3.4617 | 81.6494 | 1166.2998 | 137.7130 | 0.2933 | 9.280 | 3.4688 | 513.4421 | 933.1137 | 117.9376 | 91.2071 | 3.3613 | 56.2500 | 38.1356 | 25.3602 | 54.5557 | 69.1393 | 19.3947 | 22.4486 | 219.5726 | 218.5615 | 267.4464 | 94.6698 | 501.5730 | 426.5525 | 383.1325 | 298.4946 | 375.5372 | 464.2254 | 141.0405 | 455.8140 | 0.0 | 0.6943 | 400.444 | 81.354 | 0.5804 | 21.11 | 1.44 | 0.5784 | 262.136 | 0.6608 | 7.28 | 536.0109 | 2.2047 | 6.11 | 0.3094 | 128.2819 | 1.0 |
| 2925 | 3163.86 | 2470.60 | 2211.4000 | 1511.7842 | 1.3004 | 97.4700 | 197.9805 | 10.3963 | 404.7285 | 9.9074 | 188.0731 | 12.4694 | -5390.00 | 2596.25 | -3240.00 | -1666.6667 | 1.2668 | 70.2556 | 2.0111 | 3.2300 | 86.8990 | 9.3181 | 50.7948 | 64.1792 | 49.2052 | 65.8829 | 87.4062 | 118.7162 | 78.70 | 2.932 | 357.2109 | 10.2322 | 130.8382 | 1.4048 | 141.9236 | 190.1790 | 2854.0 | -0.0345 | 10.6889 | 110.8036 | 18.2227 | 20.0345 | 0.9239 | 143.5155 | 93.1805 | 0.0 | 7.3741 | 1787.2501 | 8989.4699 | -0.0137 | 775.5831 | 58.5854 | 6.1532 | 2.626 | 0.8089 | 2.971 | 3.413 | 1.4190 | 995.0191 | 39.8933 | 103.0 | 158.5 | 106.6 | 73.2000 | 1533.7770 | 7.06 | 11.019 | 12.380 | 0.66 | 731.0 | 339.0 | 3498.0 | 12333.0 | 0.145 | 2.3 | 1.8 | 0.404 | 0.5745 | 0.6595 | 0.259 | 15.78 | 0.267 | 8.81 | 30.868 | 0.1613 | 5.85 | 38.270 | 0.261 | 0.310 | 23.90 | 9.48 | 48.577 | 4.1524 | 123.7789 | 1411.2998 | 136.8739 | 0.3871 | 19.379 | 3.5594 | 0.0000 | 489.7666 | 105.9899 | 107.9630 | 739.9800 | 89.5028 | 76.7832 | 20.1231 | 0.0000 | 131.1551 | 46.3549 | 11.5439 | 641.8858 | 81.6420 | 94.1593 | 47.0588 | 698.7104 | 953.0612 | 0.0000 | 232.4190 | 0.0000 | 0.0000 | 46.8750 | 610.9091 | 0.0 | 0.8273 | 400.814 | 73.254 | 0.2235 | 14.53 | 1.33 | 0.9800 | 279.470 | 0.6512 | 10.83 | 533.1809 | 1.0744 | 5.65 | 0.1416 | 42.5048 | 1.0 |
2926 rows × 140 columns
print('''\n\033[1m''' + '''SHAPE OF NEW DATAFRAME''' + '''\033[0m''')
nsd.shape
SHAPE OF NEW DATAFRAME
(2926, 140)
print('''\n\033[1m''' + '''BALANCED TARGET''' + '''\033[0m''')
nsd['Pass/Fail'].value_counts()
BALANCED TARGET
1.0 1463 -1.0 1463 Name: Pass/Fail, dtype: int64
nsd=nsd.apply(zscore)
nx = nsd.drop('Pass/Fail',axis=1)
ny=nsd['Pass/Fail']
print('''\n\033[1m''' + '''Spliting New dataset in Train and Test sets''' + '''\033[0m''')
nx_train,nx_test,ny_train, ny_test = train_test_split(nx, ny,train_size=0.7, test_size=0.3,random_state=202)
print('New X_Train',len(nx_train)),print('New X_Test',len(nx_test))
print('New Y_Train',len(ny_train)),print('New Y_Test',len(ny_test))
score_train={}
score_test={}
accuracy={}
Spliting New dataset in Train and Test sets
New X_Train 2048
New X_Test 878
New Y_Train 2048
New Y_Test 878
new_sd1 = pd.concat([nx_train,nx_test], axis=0, ignore_index=True)
new_sd1
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | 18 | 19 | 21 | 22 | 23 | 24 | 25 | 28 | 29 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 43 | 44 | 45 | 47 | 48 | 51 | 55 | 59 | 61 | 62 | 63 | 64 | 67 | 68 | 71 | 74 | 83 | 88 | 90 | 96 | 115 | 117 | 120 | 122 | 125 | 126 | 128 | 129 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 142 | 150 | 151 | 155 | 159 | 160 | 161 | 162 | 163 | 166 | 167 | 169 | 170 | 175 | 177 | 180 | 181 | 182 | 183 | 184 | 185 | 188 | 195 | 198 | 200 | 201 | 208 | 218 | 223 | 225 | 250 | 255 | 268 | 269 | 418 | 419 | 423 | 432 | 433 | 438 | 439 | 460 | 468 | 472 | 474 | 476 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 499 | 500 | 510 | 511 | 521 | 546 | 547 | 548 | 549 | 550 | 551 | 559 | 562 | 563 | 564 | 570 | 571 | 572 | 573 | 589 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.064747 | -0.313718 | -0.385191 | -0.124860 | -0.040510 | 0.679484 | 1.431873 | -0.466871 | 1.341764 | -0.294925 | 1.780056 | 0.177770 | 0.026935 | -0.264984 | -1.275005 | -0.276734 | 0.502880 | 1.428983 | -2.179305 | -0.467619 | -0.047774 | -0.427485 | -0.243219 | -0.126241 | 0.243212 | -0.618441 | -0.499261 | 0.847857 | 0.611802 | -0.165411 | -0.442840 | 0.005288 | 0.649235 | 0.569495 | 1.258773 | 0.608751 | 0.026505 | -0.512432 | -0.202198 | 0.650880 | 0.144652 | -0.221136 | -0.076474 | 0.226451 | 0.290814 | -0.01849 | 1.460669 | -0.372182 | -1.148798 | 0.119069 | -0.558996 | -0.163139 | 0.416035 | 1.060131 | 0.162245 | -0.579809 | 0.426069 | -0.750362 | 0.217049 | 0.522848 | 0.407292 | -0.121218 | -1.052980 | -1.256354 | -0.101323 | 0.401123 | 0.242552 | 0.190883 | -0.227527 | -0.385487 | -0.060177 | 0.157618 | 1.179903 | 0.450234 | -0.454423 | 0.729496 | 0.376596 | -0.989870 | -0.860136 | -0.370646 | -1.977901 | -1.015619 | 0.534821 | 1.092342 | -0.339391 | -0.427527 | 0.352135 | -0.108599 | -0.389701 | 0.102051 | -0.772628 | 0.538778 | -1.578466 | -0.271368 | -0.044543 | -0.138290 | 1.740016 | -0.667559 | -1.245409 | 1.431868 | 0.681999 | 0.669089 | -0.157478 | -0.932692 | 1.577956 | 1.746304 | -0.285488 | -0.872469 | -0.273266 | -0.924075 | -0.851575 | 1.881518 | 0.298312 | -0.319867 | -0.417719 | 0.680829 | -0.892087 | 0.502744 | -1.218706 | -0.744435 | -0.748253 | 0.214044 | -0.907001 | -0.138692 | 1.718497 | 1.682522 | 2.363762 | 0.759978 | 0.133436 | -0.100046 | -0.982431 | 0.242336 | -1.070239 | -0.518533 | 0.214096 | 0.270279 | -0.197124 | -0.096382 | -0.937904 |
| 1 | 0.465627 | 0.656212 | -0.934419 | 0.956997 | -0.035881 | -0.724824 | -0.079498 | -1.730163 | -0.972754 | 0.139909 | -0.181187 | 0.100092 | 0.031449 | -0.237009 | -2.138469 | 0.009712 | 0.362974 | -1.212800 | 0.561117 | -0.693976 | -0.172802 | -0.234486 | -0.048887 | -0.047111 | 0.048880 | -1.048353 | -0.048757 | -0.349539 | 0.426295 | -0.563424 | 0.826279 | -0.430288 | -1.130661 | 0.694065 | 0.731027 | 0.387715 | -0.079250 | -1.139713 | 1.451891 | -1.595604 | 1.607078 | 0.832838 | -0.076556 | 0.087687 | 0.745037 | -0.01849 | -0.579885 | 0.414850 | 1.668403 | -1.293577 | 1.616069 | -0.141927 | 1.208984 | -0.407083 | -0.968479 | -0.122568 | -0.235613 | 1.010481 | 0.770542 | 0.240217 | 0.118312 | -0.711930 | -0.770949 | 0.129458 | -0.069080 | 0.268456 | -0.539401 | -0.027138 | 0.099171 | -0.287150 | -0.254538 | 1.049424 | -0.424258 | -0.160804 | 0.470787 | 1.818876 | -0.732006 | 1.374425 | 1.736858 | -0.121434 | -2.245856 | 0.630161 | -0.620637 | -1.304093 | -0.455497 | -0.131563 | 0.944055 | -0.119000 | -0.216892 | -0.960268 | -0.266275 | 0.538562 | 1.857119 | -0.827965 | -0.688300 | -0.421749 | 0.107137 | -1.143347 | -0.541952 | 0.459237 | -1.005759 | 1.540286 | 0.145366 | 1.404167 | 1.117502 | 0.704551 | 0.382700 | -0.421215 | -2.242094 | -0.845464 | -0.802835 | -0.774673 | -0.468944 | 1.647797 | 2.144389 | -0.682485 | 0.228017 | 0.504478 | 0.104996 | -0.744435 | -0.748253 | 1.022737 | -0.907001 | -0.138692 | -0.272716 | -0.167668 | -0.430099 | -0.261959 | -0.158174 | -0.100046 | -0.186771 | 0.272527 | 0.007205 | -0.894735 | -0.850947 | -1.096610 | 5.545473 | 4.368989 | 0.157774 |
| 2 | 1.208099 | 1.203306 | 0.617374 | 0.652699 | -0.048606 | -0.570695 | 0.710161 | -0.241396 | 0.882404 | 0.225006 | 0.634173 | -0.261598 | -0.065613 | 0.194907 | -0.269933 | 0.023088 | 0.493427 | -0.544937 | 0.617562 | -0.571524 | -0.298699 | -0.045743 | -0.124182 | -0.094651 | 0.124174 | 0.852836 | 0.254600 | -0.319323 | -0.028258 | -0.281255 | 0.724675 | -0.220853 | -0.562606 | 0.848965 | 1.201135 | 1.114825 | 0.167511 | -0.138764 | -0.464679 | 0.029573 | -0.165458 | -0.848981 | -0.076654 | 0.353169 | -0.832924 | -0.01849 | 0.188773 | 1.430813 | -0.342384 | 0.681291 | -2.481660 | -0.221772 | 0.591194 | 0.513426 | -0.301038 | -1.021681 | 0.140638 | 0.526130 | -0.658986 | -1.201726 | -0.595638 | 0.100994 | 0.099317 | 1.389311 | 0.349432 | 0.384114 | 0.421660 | 0.069464 | -0.142828 | -0.461130 | -0.120547 | 0.830439 | -0.307058 | 0.406139 | -0.974854 | -0.632230 | 1.211914 | 0.051818 | 1.526218 | -0.471776 | 0.248680 | -0.329093 | -0.544512 | 0.676429 | 0.443527 | -0.039526 | 1.119865 | -0.101665 | -0.328382 | 0.150339 | -0.584092 | -0.568195 | 0.028378 | -0.591707 | -0.326467 | -0.001885 | 2.116025 | -0.726254 | 1.303379 | 1.194027 | 0.127908 | 0.643256 | 0.581934 | 2.548983 | -0.635968 | -0.067491 | -0.025298 | -0.354663 | 0.240483 | -0.577645 | 0.610888 | -1.091019 | -0.223186 | 0.606977 | 1.185132 | 0.195062 | 1.951834 | 0.976719 | -0.265062 | 1.071155 | -0.748253 | 1.171600 | 0.366283 | -0.138692 | -0.279419 | -0.611751 | 0.943871 | 0.060577 | -0.987484 | 0.195958 | -1.130289 | -2.119505 | -0.843842 | -1.234135 | 0.200076 | 0.870354 | -0.223327 | 0.568190 | 1.491130 |
| 3 | 2.213874 | -1.417944 | 0.318135 | 0.536947 | -0.028187 | -2.064199 | -1.744289 | -0.090547 | 0.007528 | -0.375680 | -1.674757 | 0.094023 | -0.000797 | -0.157176 | -0.365550 | 0.020545 | 0.298220 | 2.297536 | -0.851556 | -0.375082 | -0.293445 | -0.019124 | 0.030691 | 0.133986 | -0.030699 | 0.027805 | 0.764724 | 0.308888 | 0.631412 | -0.367340 | -0.582644 | -1.367075 | 0.711238 | 0.499628 | -0.878382 | 0.809662 | -1.066294 | -0.705553 | -0.806035 | 0.485316 | 0.277649 | 0.103351 | -0.077743 | -0.927457 | 1.565258 | -0.01849 | 0.331313 | -1.553142 | -0.608110 | -0.326657 | -0.273955 | -0.112953 | 0.124893 | 0.599876 | 0.923913 | -0.383849 | -0.975139 | 0.350037 | -0.129322 | -0.582021 | -0.204666 | 0.775036 | 0.187955 | -0.476838 | 0.335141 | -0.384675 | -1.016716 | -0.001273 | -0.179128 | -0.236722 | -0.578474 | -0.581057 | -0.367639 | -0.028517 | 0.181659 | -0.768402 | 0.252845 | -0.535337 | -0.447746 | 0.236132 | 0.711222 | 0.263388 | -0.995821 | -0.226248 | -0.739904 | -0.191117 | 1.092959 | -0.268086 | 0.262516 | -0.312701 | -0.779810 | 1.422468 | 0.165339 | -0.588488 | -0.208609 | -0.515049 | -0.293413 | 0.513371 | 0.584786 | 2.308402 | -0.096356 | -1.182801 | -0.508523 | 2.025773 | -0.541834 | 0.187344 | 0.159682 | 0.020809 | -1.004690 | -0.712493 | -0.529253 | 0.490341 | 0.389136 | -0.770202 | 1.162943 | -1.147405 | 0.426953 | 1.050213 | -0.179615 | 0.620676 | 1.381688 | -0.825052 | 1.883106 | -0.138692 | -0.272716 | -0.167668 | -0.430099 | -0.261959 | -0.158174 | -0.100046 | -1.045234 | -1.266067 | 3.021373 | 0.487399 | 0.029213 | -0.417286 | -0.198326 | 0.404763 | 0.325123 |
| 4 | -0.720244 | -0.233140 | -0.268778 | 0.155081 | -0.010929 | 1.075359 | 0.016208 | 0.866046 | -0.038066 | -0.066572 | 0.062966 | 0.799804 | -1.054297 | -1.055806 | 0.145075 | 0.323192 | -2.575527 | 1.327009 | -1.303377 | -2.839877 | -0.643841 | -0.281569 | -0.140163 | 0.021356 | 0.140156 | -0.982902 | -0.073946 | -0.675239 | 0.562386 | -0.606999 | -1.516650 | 0.142525 | 1.119167 | 0.637738 | 0.146624 | 0.802951 | -1.277804 | -1.015556 | -0.717996 | 0.485508 | -0.053913 | 0.624225 | -0.076929 | 0.007931 | 0.032930 | -0.01849 | 0.886917 | 2.733553 | 0.181706 | 0.751695 | -0.185233 | -0.017708 | -0.861363 | -1.661216 | -0.801619 | 0.795756 | 0.802319 | 0.658200 | -0.762328 | -0.778078 | -0.136670 | -0.893403 | -0.569499 | 0.279072 | -0.330387 | -0.670419 | 1.860955 | -0.240921 | -0.239627 | -0.560307 | -0.806700 | -0.961370 | -0.698937 | 0.469132 | 0.008182 | -0.496057 | -1.180603 | -1.516139 | -0.518848 | -0.901576 | -0.918839 | -1.302455 | 1.228096 | 0.743210 | 0.168707 | -0.286764 | 0.111697 | -0.073928 | 0.383761 | -0.016080 | 1.466455 | -0.267063 | -1.049736 | 0.741189 | 0.608364 | 0.185730 | -0.049752 | 0.583993 | 0.153002 | -1.078279 | -1.005759 | 1.052121 | -0.782120 | -0.582952 | -0.189200 | 0.486418 | -0.584719 | 0.513657 | -0.208775 | 0.719951 | 0.722521 | 1.917785 | 0.832726 | -0.423861 | 0.593240 | 2.407746 | -0.892087 | -1.318982 | -1.218706 | -0.744435 | 1.482093 | -0.527473 | -0.907001 | -0.138692 | 1.341355 | 0.823806 | -0.814615 | -0.294033 | -0.247791 | -0.022629 | 0.779988 | -1.954856 | 1.661962 | 0.421972 | 0.083056 | 0.623414 | -0.202205 | -0.168468 | -0.370533 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2921 | -2.303706 | -2.724005 | 0.338656 | 1.890460 | -0.042996 | -1.107467 | -1.008629 | 0.156209 | 1.054760 | -0.169089 | -1.000539 | 0.342837 | 0.156244 | -0.406227 | 0.079815 | -0.969725 | 0.636169 | 0.418987 | 1.295676 | -0.465625 | -0.575579 | -0.150423 | -0.371566 | 0.067652 | 0.371558 | -0.453779 | 0.057436 | -0.448502 | -2.033936 | 1.022784 | -0.547187 | -0.130158 | -0.166387 | 0.662110 | 0.318362 | 1.126997 | -0.431766 | -0.768013 | -0.040520 | -0.342410 | -0.091787 | 0.208298 | -0.076506 | 0.470230 | 0.311973 | -0.01849 | 1.069535 | -0.547693 | -1.770298 | -1.069195 | -0.371859 | -0.062725 | 0.065717 | 3.262778 | 1.870108 | -0.987100 | -0.036676 | -1.190596 | 0.731458 | -0.763227 | -0.442649 | 0.510234 | 2.077559 | 2.971949 | 0.287649 | -0.000280 | -0.030550 | 0.024243 | -0.300127 | -0.147630 | -0.314908 | -0.747940 | 0.984789 | -0.274192 | -0.570075 | -0.359885 | 0.520972 | -0.981430 | -0.599726 | 2.309288 | -0.006515 | -0.394925 | -1.262256 | -0.336746 | -0.746295 | -0.517760 | 1.527977 | 0.695771 | -0.593171 | 0.549571 | 0.353198 | 0.479733 | -1.655345 | -0.210080 | -0.760622 | -0.791570 | -0.045370 | -0.774646 | -0.350633 | -1.078279 | 0.243062 | -0.218375 | -0.598751 | 0.571783 | -0.532339 | -0.203021 | 0.250851 | 0.878968 | 0.793240 | 0.427076 | -0.356286 | -1.091019 | -1.137218 | 0.239612 | -0.683099 | 0.358661 | -0.047747 | -1.318982 | -0.308748 | 1.094607 | -0.318367 | -0.557702 | -0.907001 | -0.138692 | 0.222173 | 1.654475 | -0.351114 | 0.154998 | 0.140548 | 0.145865 | 0.317131 | -0.238474 | -0.503218 | -0.367234 | 0.152142 | 0.802200 | -0.178139 | -0.234135 | -0.856018 |
| 2922 | -0.399083 | -2.577844 | 0.599834 | 0.361609 | -0.048160 | 0.793741 | -1.162657 | 0.839196 | -0.510988 | 0.622550 | -1.701396 | -0.186347 | 0.300709 | -0.103272 | -0.043069 | 2.589047 | 0.326580 | 0.692042 | -1.218708 | -0.553575 | -0.024704 | 0.017496 | -0.100045 | -0.312803 | 0.100037 | -1.847893 | -0.948678 | -0.047145 | 0.765542 | -0.799894 | -0.650781 | -0.487569 | -0.732635 | -1.018497 | -0.524750 | -1.603907 | -1.912332 | -1.067671 | 1.223775 | -1.170628 | 0.709107 | 0.711792 | -0.076512 | -0.263353 | -0.260486 | -0.01849 | -0.025604 | 0.097922 | -0.685759 | 0.779047 | -0.095629 | 0.182454 | -0.425043 | -0.727313 | -0.414895 | -0.122568 | 0.270379 | 0.085896 | 0.236809 | -0.153024 | -0.476647 | -1.298939 | 0.335014 | 0.940478 | -0.268169 | -0.486726 | 2.307960 | -0.264689 | 0.062871 | -0.449364 | -0.273680 | -0.796603 | 2.706975 | -0.362383 | 0.239484 | 0.457151 | 0.087844 | -1.309972 | 0.835642 | -0.276740 | -0.271280 | 1.048660 | -0.552669 | 1.006566 | -0.182808 | 0.597519 | -1.198580 | -0.054859 | -0.339531 | -0.053157 | -0.356054 | 0.553368 | 1.073495 | 0.706375 | 0.028447 | -0.062686 | 0.224585 | -0.287607 | 1.091823 | -1.078279 | 0.798200 | 1.472502 | -0.649335 | 0.437136 | 0.719088 | 1.747381 | 0.215105 | -0.399164 | -0.984906 | -0.899562 | -0.378273 | -0.253778 | 1.040160 | -0.491193 | 0.329954 | -0.223401 | -0.892087 | 0.053420 | -0.368840 | -0.744435 | 0.793515 | -0.315368 | 0.455141 | -0.138692 | 0.449798 | -0.724408 | -0.341318 | 0.511735 | -0.469699 | -0.213893 | -0.354903 | 0.242336 | -1.070239 | -0.518533 | 0.031778 | 0.959747 | -0.229209 | -0.266722 | -1.133444 |
| 2923 | 0.576069 | 0.373409 | -0.643015 | -1.102484 | -0.035039 | 0.474043 | -0.323119 | -1.148652 | -0.242270 | -0.056276 | -0.318821 | 0.180197 | -0.005634 | -0.202210 | 1.859460 | -0.554514 | 0.500517 | 1.600064 | 0.250410 | -0.447676 | -0.743705 | -0.436358 | -0.221142 | -0.132284 | 0.221134 | -0.975668 | -0.330005 | 0.673003 | 0.631020 | -0.126619 | -0.161725 | -0.331239 | 0.849319 | 0.750934 | 0.903964 | 1.177303 | 0.273266 | -0.831423 | -0.310529 | 0.461638 | 0.156787 | 0.314842 | -0.076462 | 0.561731 | 0.643739 | -0.01849 | 1.075962 | -1.941966 | -0.031675 | -0.108352 | -0.789426 | -0.405371 | 0.511505 | 1.258600 | 0.013052 | 0.180978 | 0.110365 | -0.046174 | -0.214152 | -0.462929 | 0.900258 | -0.937845 | -0.821312 | -0.571326 | -0.445391 | -0.510538 | 1.151870 | 0.157983 | -0.251727 | -0.782194 | -0.303129 | 2.494773 | 0.023580 | -0.141906 | -0.512249 | 0.184806 | -0.169971 | 0.037350 | 0.338818 | -0.258681 | -0.765722 | 0.569032 | 1.073129 | 1.359617 | 0.279487 | 0.877241 | 0.373817 | -0.132869 | 0.304324 | 0.271057 | -0.388375 | 1.763480 | -1.185925 | -0.163103 | -0.063294 | -0.143323 | 1.198353 | 0.406691 | -1.747768 | -0.129220 | 1.285107 | 3.388639 | -0.834416 | 0.143346 | 0.019928 | 0.358523 | -0.574485 | -0.053473 | -0.036484 | -0.676350 | -0.188717 | -1.091019 | -0.291595 | -0.685951 | -0.320961 | -1.147405 | 0.524291 | -1.318982 | -0.761385 | 1.152322 | -0.748253 | 0.096663 | -0.907001 | -0.138692 | -0.731755 | -1.327894 | 1.714127 | -0.263432 | -1.256334 | 0.022910 | -0.794519 | 0.242336 | -1.070239 | -0.518533 | 0.168565 | 0.223681 | -0.185626 | 1.016505 | -0.896468 |
| 2924 | -0.004881 | 0.363630 | -0.768008 | -0.723478 | -0.037150 | -0.034899 | 0.609794 | -0.678904 | -0.319537 | -0.039189 | 0.700434 | -0.196057 | 0.352949 | -0.177646 | 0.061637 | 0.297760 | 0.558181 | -0.518644 | 0.222187 | -0.264597 | -0.640404 | 0.012470 | -0.134808 | -0.083410 | 0.134801 | 1.220742 | 0.181932 | -0.236639 | 0.067045 | 0.309123 | -0.680600 | 0.675360 | -1.318131 | 1.286042 | 1.080083 | 0.995533 | -0.995791 | 1.742666 | 0.220325 | -0.623173 | -0.326756 | -0.847257 | -0.076658 | 0.535835 | -0.490436 | -0.01849 | 0.869903 | 0.002584 | -0.558816 | -0.849878 | 0.090831 | 0.044080 | 0.083864 | 1.057696 | 0.032683 | -0.502962 | -0.131820 | 0.702317 | -0.197050 | -0.057934 | -0.459648 | -1.195240 | 0.177882 | -0.957159 | -0.552001 | -0.738454 | -1.469845 | 0.302733 | 0.014471 | -0.139226 | 0.297623 | 0.614629 | -0.652056 | 0.254954 | 0.066007 | 1.001841 | 1.196445 | -0.640832 | 0.615226 | -0.399540 | -0.344649 | -1.264837 | -1.003977 | 0.326393 | -0.248850 | -0.355341 | -0.025188 | -0.042724 | -0.363222 | -0.212678 | -0.722352 | 0.077155 | -0.780358 | -0.368272 | 0.437604 | 0.028516 | 1.300901 | -0.964961 | 1.465872 | 0.062473 | -1.005759 | -1.534251 | 0.570504 | -0.219467 | 0.640124 | -0.169549 | -0.775175 | -0.797423 | -0.160764 | -0.861627 | -0.172595 | -1.091019 | -0.605543 | 0.444760 | -0.168797 | 0.491113 | -0.490016 | 0.625340 | 0.021949 | -0.744435 | -0.748253 | -0.654663 | -0.907001 | 6.166954 | -0.272716 | -0.167668 | -0.430099 | -0.261959 | -0.158174 | -0.100046 | 0.389823 | 0.242336 | -0.206843 | -0.444928 | 0.059656 | 1.235534 | -0.229744 | 0.147031 | 0.440024 |
| 2925 | -0.568009 | 1.753268 | 0.820347 | -1.082388 | -0.037581 | 0.622454 | -1.520423 | 0.369589 | 0.690372 | -0.085272 | -1.622114 | -0.206981 | 5.268312 | -5.583755 | 2.014883 | 0.198667 | -4.547447 | -1.893809 | 0.109041 | -0.331805 | -0.164664 | 0.025292 | -0.327987 | -0.126063 | 0.327980 | 0.920354 | 2.644845 | -0.235936 | 0.357269 | -0.764822 | -0.171011 | -0.927919 | 0.183586 | -1.561729 | -0.612526 | -0.369246 | 1.084052 | 1.130698 | 0.058974 | -0.022149 | -0.611127 | 0.180986 | -0.076573 | 0.549349 | -0.740519 | -0.01849 | -0.166632 | 0.551995 | -0.408780 | 0.662044 | -0.178496 | -0.386256 | 0.838941 | -0.881949 | -1.154184 | -0.791139 | -0.179391 | 0.306013 | -1.035379 | -0.218412 | -0.765627 | 1.284270 | 1.453063 | 0.436536 | -0.437550 | 0.492969 | 0.229387 | -0.110545 | 0.026571 | 3.968210 | 2.730081 | 0.064259 | -0.675166 | 4.866084 | -0.396598 | -0.359885 | -1.386854 | -0.612499 | 0.288158 | 1.485805 | -0.000135 | 0.898188 | -0.781041 | -0.646231 | -0.500235 | 0.417053 | -0.562881 | 0.047421 | -0.141636 | -0.093684 | -0.761854 | -0.203623 | -0.362619 | 0.736601 | -0.213296 | 0.753621 | -0.778982 | 2.560072 | 0.373231 | 1.349861 | 0.197639 | -0.092686 | 2.539483 | 0.755738 | -0.364061 | -0.450019 | -0.466589 | -0.775231 | 0.658585 | -0.617147 | -0.317319 | 2.097517 | -0.565130 | -1.005309 | -0.117627 | -0.599336 | -0.574686 | 0.261256 | 0.179497 | 0.320393 | 1.349369 | 1.228317 | 1.225081 | -0.138692 | 0.167088 | -1.130160 | 1.820052 | 0.159089 | -0.466854 | -0.141031 | -1.015563 | 0.242336 | -0.206843 | -0.444928 | 0.123327 | 0.591397 | -0.171588 | 0.218623 | 0.474625 |
2926 rows × 139 columns
a=nsd.drop('Pass/Fail',axis=1)
a.describe()
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | 18 | 19 | 21 | 22 | 23 | 24 | 25 | 28 | 29 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 43 | 44 | 45 | 47 | 48 | 51 | 55 | 59 | 61 | 62 | 63 | 64 | 67 | 68 | 71 | 74 | 83 | 88 | 90 | 96 | 115 | 117 | 120 | 122 | 125 | 126 | 128 | 129 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 142 | 150 | 151 | 155 | 159 | 160 | 161 | 162 | 163 | 166 | 167 | 169 | 170 | 175 | 177 | 180 | 181 | 182 | 183 | 184 | 185 | 188 | 195 | 198 | 200 | 201 | 208 | 218 | 223 | 225 | 250 | 255 | 268 | 269 | 418 | 419 | 423 | 432 | 433 | 438 | 439 | 460 | 468 | 472 | 474 | 476 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 499 | 500 | 510 | 511 | 521 | 546 | 547 | 548 | 549 | 550 | 551 | 559 | 562 | 563 | 564 | 570 | 571 | 572 | 573 | 589 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 |
| mean | -3.944669e-15 | 4.975894e-16 | 2.519060e-16 | -8.362719e-17 | -4.731775e-17 | 2.607468e-16 | 5.343565e-16 | -7.021041e-16 | -6.631363e-16 | -2.527787e-16 | 7.611250e-15 | 7.346292e-15 | -6.033945e-17 | 4.451611e-16 | 1.106808e-16 | 4.270526e-17 | 1.544675e-16 | -3.220975e-15 | -1.047237e-17 | 9.531375e-16 | 2.800676e-15 | 2.358702e-16 | 7.400987e-15 | -4.452465e-16 | -3.372587e-15 | -9.008894e-15 | -1.361932e-14 | -2.493719e-15 | -5.691506e-19 | 5.829999e-17 | 3.446453e-15 | 3.597487e-15 | -2.140803e-15 | -6.675757e-16 | 8.754864e-16 | -2.579770e-16 | -9.218247e-16 | 3.151956e-16 | -2.718984e-15 | 3.471818e-16 | -3.409212e-17 | -4.336263e-16 | 8.257900e-17 | -1.652016e-15 | 2.158409e-16 | 2.683308e-16 | -5.996807e-16 | 4.936736e-15 | 1.592787e-15 | 5.881222e-18 | -2.288175e-15 | 6.401995e-17 | -2.970966e-16 | -5.595888e-16 | 1.041166e-16 | -2.533858e-16 | -5.962801e-16 | -3.437669e-16 | -2.368987e-14 | -7.470860e-16 | 2.078822e-16 | 1.142095e-17 | 1.799464e-16 | 7.633447e-16 | -1.548090e-16 | -7.019524e-17 | 4.135827e-17 | -4.793433e-17 | -1.042684e-16 | -1.034526e-16 | -2.314735e-16 | -1.896837e-16 | -5.903988e-17 | -1.546335e-16 | 5.165990e-17 | -1.792445e-16 | 2.532364e-16 | -1.602728e-16 | -6.231819e-16 | -3.490031e-16 | -2.488326e-16 | 1.523806e-16 | -3.311887e-16 | -6.628327e-16 | -2.447158e-16 | -1.179280e-16 | -1.735340e-16 | 1.372791e-16 | -1.509956e-16 | 1.944218e-16 | 1.940424e-16 | -9.383396e-17 | -3.923345e-17 | -2.282460e-16 | 4.090295e-17 | -2.663245e-16 | 2.169317e-16 | 2.806292e-16 | -4.642941e-16 | -7.323071e-17 | 4.335789e-16 | -7.888427e-17 | -4.611068e-17 | 1.487380e-16 | 8.844600e-17 | -1.272431e-16 | -1.145510e-16 | -2.097510e-16 | -1.988422e-16 | -3.429891e-16 | 2.777455e-16 | -3.681266e-16 | -5.945726e-17 | -2.983867e-16 | 8.628322e-17 | 5.550356e-16 | 3.460435e-17 | 2.073985e-16 | -3.170169e-17 | 5.443356e-16 | -1.365961e-17 | -3.621695e-17 | -5.421349e-16 | 4.060700e-16 | 2.355145e-16 | -1.053228e-14 | -2.101456e-15 | 5.312831e-16 | 4.736281e-16 | -3.115151e-17 | -5.630796e-17 | 6.379950e-15 | 2.609555e-17 | 1.298422e-16 | -1.676566e-15 | 1.019918e-15 | -8.392125e-17 | 9.546552e-17 | -1.478653e-16 |
| std | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 |
| min | -3.338221e+00 | -4.384322e+00 | -4.685578e+00 | -3.484829e+00 | -5.245552e-02 | -3.412454e+00 | -6.006751e+00 | -2.292982e+00 | -5.852905e+00 | -3.042174e+00 | -7.747908e+00 | -1.586949e+01 | -2.108119e+00 | -7.274572e+00 | -4.501270e+00 | -5.428998e+00 | -5.647789e+00 | -2.811691e+00 | -4.354505e+00 | -3.104923e+00 | -9.189028e-01 | -7.940465e-01 | -6.205154e-01 | -3.670969e-01 | -7.564916e+00 | -4.542077e+00 | -3.876230e+00 | -4.067862e+00 | -2.511079e+00 | -1.839246e+00 | -2.102006e+00 | -3.341489e+00 | -3.459809e+00 | -3.731950e+00 | -3.054151e+00 | -1.840744e+00 | -1.947584e+00 | -3.257909e+00 | -3.174584e+00 | -3.697490e+00 | -1.766382e+00 | -2.335709e+00 | -7.782794e-02 | -1.061078e+01 | -2.454902e+00 | -1.849001e-02 | -3.037851e+00 | -3.440714e+00 | -3.918944e+00 | -7.564633e+00 | -4.172798e+00 | -1.165962e+00 | -9.313965e+00 | -2.546414e+00 | -3.252697e+00 | -1.682567e+00 | -1.389307e+01 | -3.211206e+00 | -3.954851e+00 | -2.069502e+00 | -9.696125e-01 | -1.921131e+00 | -2.146855e+00 | -3.051629e+00 | -1.542735e+00 | -1.633106e+00 | -1.678345e+00 | -4.710887e-01 | -3.848262e-01 | -8.443901e-01 | -9.186053e-01 | -1.020348e+00 | -7.621587e-01 | -9.986183e-01 | -1.206156e+00 | -1.313092e+00 | -1.804513e+00 | -2.414957e+00 | -2.903425e+00 | -1.226636e+00 | -2.915744e+00 | -2.059514e+00 | -2.020780e+00 | -3.351246e+00 | -1.240546e+00 | -9.635108e-01 | -1.748800e+00 | -4.293068e-01 | -7.004797e-01 | -1.312075e+00 | -1.463924e+00 | -2.417641e+00 | -2.320950e+00 | -1.877159e+00 | -1.978939e+00 | -1.766472e+00 | -2.419749e+00 | -1.807824e+00 | -2.116715e+00 | -1.078279e+00 | -1.005759e+00 | -1.734599e+00 | -8.344158e-01 | -9.326917e-01 | -1.405784e+00 | -1.642950e+00 | -1.656651e+00 | -8.724692e-01 | -2.564855e+00 | -1.470216e+00 | -1.284634e+00 | -1.091019e+00 | -1.137218e+00 | -1.005309e+00 | -8.918423e-01 | -1.147405e+00 | -8.920867e-01 | -1.318982e+00 | -1.218706e+00 | -7.444346e-01 | -7.482532e-01 | -1.304467e+00 | -9.070010e-01 | -1.386918e-01 | -1.632930e+00 | -7.249625e+00 | -1.362611e+00 | -9.597239e-01 | -1.613378e+00 | -5.690974e-01 | -1.279630e+00 | -2.830657e+00 | -3.768481e+00 | -2.158284e+00 | -9.536343e+00 | -3.531461e+00 | -2.769371e-01 | -1.274936e+00 | -1.133444e+00 |
| 25% | -6.886170e-01 | -5.418254e-01 | -7.254718e-01 | -7.502949e-01 | -4.396021e-02 | -4.508966e-01 | -5.557568e-01 | -7.498768e-01 | -4.691685e-01 | -1.754332e-01 | -5.871694e-01 | -1.863474e-01 | -3.147996e-01 | -2.390558e-01 | -4.558051e-01 | -4.067933e-01 | 2.391383e-01 | -5.679729e-01 | -7.384097e-01 | -5.096001e-01 | -3.815183e-01 | -2.751577e-01 | -2.774890e-01 | -2.179227e-01 | 2.927340e-02 | -5.977723e-01 | -5.187869e-01 | -3.731964e-01 | 1.096162e-02 | -3.838134e-01 | -7.554844e-01 | -7.095340e-01 | -7.791092e-01 | -9.966976e-01 | -6.531232e-01 | -6.970810e-01 | -8.547846e-01 | -6.197928e-01 | -5.739915e-01 | -5.168353e-01 | -5.663905e-01 | -6.823527e-01 | -7.681758e-02 | -3.961743e-01 | -5.348570e-01 | -1.849001e-02 | -6.526672e-01 | -5.662601e-01 | -6.293830e-01 | -5.019078e-01 | -4.633043e-01 | -1.094815e-01 | -5.733761e-01 | -7.711466e-01 | -6.901167e-01 | -7.066072e-01 | -5.426677e-01 | -2.663841e-01 | -5.957160e-01 | -7.355657e-01 | -4.426491e-01 | -9.008100e-01 | -7.276373e-01 | -5.319717e-01 | -6.204448e-01 | -5.003332e-01 | -7.146051e-01 | -2.141055e-01 | -2.396272e-01 | -4.779399e-01 | -4.500038e-01 | -6.666802e-01 | -6.793756e-01 | -4.127780e-01 | -4.544233e-01 | -4.960570e-01 | -7.784124e-01 | -7.409014e-01 | -7.365970e-01 | -5.765170e-01 | -6.732134e-01 | -7.887363e-01 | -7.232686e-01 | -8.486846e-01 | -4.618885e-01 | -3.282711e-01 | -8.123687e-01 | -1.658063e-01 | -4.050311e-01 | -3.247731e-01 | -5.499764e-01 | -5.933315e-01 | -7.314829e-01 | -5.624802e-01 | -6.589478e-01 | -6.318598e-01 | -7.491819e-01 | -8.363218e-01 | -7.742550e-01 | -1.078279e+00 | -1.005759e+00 | -7.085071e-01 | -5.648404e-01 | -8.856361e-01 | -4.750652e-01 | -6.062059e-01 | -6.513860e-01 | -7.381417e-01 | -6.585759e-01 | -5.748665e-01 | -5.744166e-01 | -1.091019e+00 | -6.395150e-01 | -6.672101e-01 | -6.595630e-01 | -1.147405e+00 | -6.138900e-01 | -9.194146e-01 | -7.018020e-01 | -7.444346e-01 | -7.482532e-01 | -5.519460e-01 | -9.070010e-01 | -1.386918e-01 | -5.863198e-01 | -7.071125e-01 | -6.841979e-01 | -4.795198e-01 | -4.156441e-01 | -2.093393e-01 | -8.375412e-01 | -9.031733e-02 | -8.983833e-01 | -5.185329e-01 | 4.669435e-02 | -3.811488e-01 | -2.243963e-01 | -5.002600e-01 | -6.241772e-01 |
| 50% | -1.419291e-01 | 5.729133e-02 | -7.139848e-02 | -2.357250e-01 | -3.714983e-02 | 4.738815e-02 | -8.037606e-02 | 4.458867e-02 | -3.980294e-02 | -2.310509e-02 | -1.504068e-01 | 3.576423e-02 | 3.789857e-02 | -3.776817e-02 | -1.780168e-02 | 1.193549e-01 | 4.140209e-01 | -5.475853e-02 | -3.850667e-03 | -3.902394e-01 | -1.293088e-01 | -1.805298e-01 | -1.637235e-01 | -1.518329e-01 | 1.637159e-01 | 1.058072e-02 | -7.394574e-02 | -2.104048e-01 | 4.886535e-01 | -1.600970e-01 | -2.076568e-01 | -3.065071e-03 | -2.223488e-02 | 3.858905e-01 | 1.358678e-02 | 4.584553e-01 | -7.924980e-02 | -2.962081e-01 | 1.053626e-02 | 1.054975e-02 | -1.903345e-01 | -1.577344e-01 | -7.661549e-02 | 4.772029e-02 | -4.920217e-02 | -1.849001e-02 | -1.482858e-02 | -2.321794e-02 | -2.703940e-03 | -5.415619e-02 | 8.942053e-02 | 1.635143e-02 | 2.232213e-02 | -1.635618e-01 | -1.714758e-01 | -1.187258e-01 | 2.387019e-02 | 2.619898e-01 | -5.019843e-02 | -1.652009e-01 | -1.536691e-01 | -1.156624e-01 | 1.873640e-02 | -1.618789e-01 | -3.080484e-01 | -9.552838e-02 | -2.659927e-01 | -7.338577e-02 | -1.307279e-01 | -2.879909e-01 | -2.368693e-01 | -2.704324e-01 | -4.666815e-01 | -1.923005e-01 | -1.652951e-01 | -8.753947e-02 | 1.049937e-02 | -9.670188e-03 | -2.469002e-02 | -3.598107e-01 | -8.945362e-02 | -9.868414e-02 | -1.502973e-01 | 2.342863e-01 | -1.540473e-01 | -1.153214e-01 | -6.221787e-02 | -1.085990e-01 | -2.001682e-01 | -6.781610e-02 | -2.420351e-01 | 7.014277e-03 | -7.909889e-02 | -6.579635e-02 | -1.965544e-01 | -1.193301e-01 | -4.975227e-02 | -1.976003e-01 | -8.491326e-02 | -7.984399e-02 | -3.645515e-02 | -2.918481e-01 | -3.321822e-01 | -2.860058e-01 | -1.621979e-01 | -1.035702e-01 | -2.714184e-01 | -3.915407e-01 | -2.554387e-02 | -1.864888e-01 | -2.615605e-01 | -9.941606e-02 | -2.799258e-01 | -3.006166e-01 | -3.626011e-01 | -2.109935e-01 | -4.713088e-01 | 1.896001e-02 | -2.399886e-01 | -7.444346e-01 | -7.482532e-01 | -2.763789e-01 | -3.374926e-01 | -1.386918e-01 | -2.727156e-01 | -1.676684e-01 | -4.300992e-01 | -2.619593e-01 | -1.581741e-01 | -1.000457e-01 | -1.689690e-01 | 2.423364e-01 | -2.068432e-01 | -4.449282e-01 | 1.062042e-01 | 1.607569e-01 | -2.077516e-01 | -1.684677e-01 | -3.025251e-01 |
| 75% | 5.641467e-01 | 5.229921e-01 | 5.930277e-01 | 5.011071e-01 | -3.222893e-02 | 5.182499e-01 | 6.856686e-01 | 7.011072e-01 | 4.233169e-01 | 1.322284e-01 | 7.720682e-01 | 3.367680e-01 | 2.562893e-01 | 3.284730e-01 | 3.540635e-01 | 6.371434e-01 | 5.194232e-01 | 6.328238e-01 | 7.024854e-01 | -2.093535e-01 | 9.025109e-02 | 6.353720e-03 | -2.919851e-02 | -9.277414e-02 | 2.774813e-01 | 4.690846e-01 | 2.763457e-01 | 1.003045e-01 | 5.623859e-01 | 1.996561e-01 | 8.595941e-01 | 7.174258e-01 | 6.367453e-01 | 8.508604e-01 | 6.672873e-01 | 8.029509e-01 | 6.610334e-01 | 4.179721e-01 | 6.183012e-01 | 5.107206e-01 | 3.382889e-01 | 5.152500e-01 | -7.643957e-02 | 4.677844e-01 | 2.890000e-01 | -1.849001e-02 | 6.570854e-01 | 6.249166e-01 | 5.647721e-01 | 5.542846e-01 | 5.912832e-01 | 1.309247e-01 | 5.233398e-01 | 6.169227e-01 | 5.313003e-01 | 4.153625e-01 | 5.428362e-01 | 5.261303e-01 | 8.180875e-01 | 5.267891e-01 | 1.693085e-01 | 8.014232e-01 | 7.681319e-01 | 3.892995e-01 | 2.845114e-01 | 2.990713e-01 | 4.395709e-01 | 5.205284e-02 | 3.867095e-02 | 1.269154e-02 | 6.081959e-03 | 2.251252e-01 | 3.639563e-01 | 1.557832e-02 | 1.816586e-01 | 3.209781e-01 | 7.117543e-01 | 7.031748e-01 | 6.167817e-01 | 9.888428e-02 | 6.506134e-01 | 6.242836e-01 | 6.775543e-01 | 7.350699e-01 | 2.635089e-01 | 2.004938e-01 | 5.795831e-01 | -4.792457e-02 | 2.137386e-01 | 1.874164e-01 | 2.400769e-01 | 6.265793e-01 | 5.931050e-01 | 4.448246e-01 | 4.349262e-01 | 4.280940e-01 | 6.227256e-01 | 7.202231e-01 | 5.169109e-01 | 6.809268e-01 | 7.850633e-01 | 5.729527e-01 | 1.477770e-01 | 4.318204e-01 | 2.435137e-01 | 4.343347e-01 | 4.694652e-01 | 3.594144e-01 | 6.585850e-01 | 2.530110e-01 | 2.827508e-01 | 7.205077e-01 | 3.312635e-01 | 3.691622e-01 | 3.299544e-01 | 6.570853e-01 | 5.047076e-01 | 6.327560e-01 | 4.251902e-01 | 8.365717e-01 | 7.050832e-01 | 1.426466e-01 | 9.721619e-01 | -1.386918e-01 | 5.022600e-01 | 7.456235e-01 | 5.452726e-01 | 1.963577e-01 | 1.842895e-01 | -1.807553e-02 | 5.705649e-01 | 2.510022e-01 | 4.723480e-01 | 4.914880e-01 | 2.045177e-01 | 6.543207e-01 | -1.873971e-01 | 2.028236e-01 | 2.352557e-01 |
| max | 4.356449e+00 | 4.582089e+00 | 3.864230e+00 | 5.932152e+00 | 2.706863e+01 | 4.956723e+00 | 2.437031e+01 | 3.842126e+00 | 3.031353e+01 | 5.172143e+01 | 9.571230e+00 | 3.019100e+00 | 7.114757e+00 | 2.704520e+00 | 4.478573e+00 | 5.463870e+00 | 8.880947e-01 | 2.665991e+00 | 2.877685e+00 | 2.348775e+00 | 7.938115e+00 | 7.279888e+00 | 7.564915e+00 | 1.322262e+01 | 6.205074e-01 | 5.927726e+00 | 2.803591e+00 | 8.625779e+00 | 8.102523e-01 | 1.833026e+01 | 3.678059e+00 | 6.139754e+00 | 4.891519e+00 | 1.762115e+00 | 5.107343e+00 | 1.612531e+00 | 2.811380e+00 | 1.529686e+01 | 4.430823e+00 | 1.825759e+01 | 2.337051e+01 | 4.391881e+00 | 1.511682e+01 | 3.682250e+00 | 4.037195e+00 | 5.408327e+01 | 2.944863e+00 | 5.540444e+00 | 5.456301e+00 | 1.264295e+01 | 3.736544e+00 | 5.257469e+01 | 9.591365e+00 | 3.807047e+00 | 5.199461e+00 | 4.661172e+00 | 2.951703e+00 | 2.593741e+00 | 2.617459e+00 | 8.756731e+00 | 1.494128e+01 | 2.887897e+00 | 4.208905e+00 | 6.554609e+00 | 5.078330e+00 | 3.294539e+01 | 4.745362e+00 | 2.288400e+01 | 1.482477e+01 | 5.703806e+00 | 5.221436e+00 | 9.014579e+00 | 5.324178e+00 | 5.029867e+00 | 1.053245e+01 | 2.047451e+01 | 3.851932e+00 | 2.739832e+00 | 3.410417e+00 | 5.314282e+00 | 9.611167e+00 | 1.430424e+01 | 1.207037e+01 | 6.889493e+00 | 3.283076e+01 | 4.703498e+01 | 6.813646e+00 | 7.818950e+00 | 1.313404e+01 | 1.562381e+01 | 2.125549e+01 | 3.598155e+00 | 4.336380e+00 | 3.449155e+01 | 5.682970e+00 | 1.998597e+01 | 4.579806e+00 | 2.903561e+00 | 5.409498e+00 | 2.425420e+00 | 2.112177e+00 | 1.034063e+01 | 7.552133e+00 | 3.270419e+00 | 2.008285e+01 | 1.750937e+01 | 8.696775e+00 | 3.513452e+00 | 7.250938e+00 | 1.440685e+01 | 1.557880e+01 | 2.442253e+00 | 4.590719e+00 | 4.080271e+00 | 3.829736e+00 | 2.448057e+00 | 2.964948e+00 | 2.640371e+00 | 3.120455e+00 | 2.346523e+00 | 2.255183e+00 | 7.804406e+00 | 2.018111e+00 | 7.984870e+00 | 8.667629e+00 | 4.174997e+00 | 2.519895e+00 | 1.052950e+01 | 1.624879e+01 | 1.728674e+01 | 3.684222e+00 | 6.829999e+00 | 6.459521e+00 | 1.076753e+01 | 2.672819e+00 | 2.045460e+00 | 5.752829e+00 | 9.241693e+00 | 7.163063e+00 |
new_sd1.describe()
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | 18 | 19 | 21 | 22 | 23 | 24 | 25 | 28 | 29 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 43 | 44 | 45 | 47 | 48 | 51 | 55 | 59 | 61 | 62 | 63 | 64 | 67 | 68 | 71 | 74 | 83 | 88 | 90 | 96 | 115 | 117 | 120 | 122 | 125 | 126 | 128 | 129 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 142 | 150 | 151 | 155 | 159 | 160 | 161 | 162 | 163 | 166 | 167 | 169 | 170 | 175 | 177 | 180 | 181 | 182 | 183 | 184 | 185 | 188 | 195 | 198 | 200 | 201 | 208 | 218 | 223 | 225 | 250 | 255 | 268 | 269 | 418 | 419 | 423 | 432 | 433 | 438 | 439 | 460 | 468 | 472 | 474 | 476 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 499 | 500 | 510 | 511 | 521 | 546 | 547 | 548 | 549 | 550 | 551 | 559 | 562 | 563 | 564 | 570 | 571 | 572 | 573 | 589 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 | 2.926000e+03 |
| mean | -3.997068e-15 | 4.997901e-16 | 2.830196e-16 | -1.358373e-17 | -6.713368e-17 | 1.776129e-16 | 4.975894e-16 | -4.151194e-16 | -6.758094e-16 | -2.331810e-16 | 7.514457e-15 | 7.465368e-15 | -2.610504e-17 | 3.366336e-16 | 1.276415e-16 | 1.201856e-17 | 1.253649e-16 | -3.007467e-15 | -7.124816e-17 | 7.169779e-16 | 2.470189e-15 | -1.316754e-17 | 7.389452e-15 | -5.676139e-16 | -3.288021e-15 | -8.869604e-15 | -1.367843e-14 | -2.521081e-15 | 9.368218e-17 | -7.774597e-17 | 3.386655e-15 | 3.669503e-15 | -2.117733e-15 | -6.986133e-16 | 9.653552e-16 | -1.967364e-16 | -9.684666e-16 | -3.581854e-17 | -2.718453e-15 | 3.350020e-16 | 6.412430e-18 | -1.551789e-16 | 1.307149e-17 | -1.785274e-15 | 2.225758e-16 | -8.972896e-16 | -5.400006e-16 | 4.973313e-15 | 1.684913e-15 | 8.802862e-18 | -2.331307e-15 | 7.533656e-17 | -3.203179e-16 | -7.933579e-16 | 3.999990e-16 | -2.252698e-16 | -7.802106e-16 | 9.614850e-17 | -2.334322e-14 | -7.726409e-16 | 1.205461e-16 | 4.151005e-17 | 1.265032e-16 | 5.687522e-16 | -3.485099e-17 | -8.062966e-17 | 8.347542e-19 | -2.064119e-17 | -1.708400e-17 | 1.548090e-17 | -3.490790e-17 | -1.435208e-17 | 5.331044e-17 | -3.672918e-17 | 6.146826e-18 | -1.495728e-16 | 2.298609e-16 | -9.538963e-17 | -5.496666e-16 | -1.237713e-16 | -2.894841e-16 | 1.932076e-16 | -2.731923e-16 | -4.670449e-16 | -2.367666e-16 | -1.573891e-16 | -1.134886e-16 | -8.870449e-17 | -1.947443e-17 | 3.909590e-17 | 2.231070e-17 | 8.682392e-17 | -1.040407e-16 | -2.311131e-16 | 2.333422e-16 | -2.431791e-16 | 3.160303e-16 | 1.781821e-16 | -4.748423e-16 | 2.309234e-16 | -2.175104e-17 | -6.306662e-17 | -3.763982e-17 | 1.444125e-16 | 1.565543e-16 | -2.045337e-16 | 5.141327e-18 | 1.290075e-16 | -2.922778e-16 | 4.864340e-17 | 1.168846e-16 | -3.703273e-17 | 1.358373e-17 | -1.199769e-16 | 3.220918e-17 | -8.651089e-17 | 4.742921e-17 | 7.909296e-17 | -6.830755e-17 | 2.715417e-16 | -4.477318e-18 | 1.490416e-16 | -6.048173e-17 | -3.406746e-16 | 2.088783e-16 | -1.082054e-14 | -2.300582e-15 | -3.268821e-17 | 1.396885e-16 | -1.126634e-16 | -7.361014e-17 | 6.862343e-15 | 1.119519e-16 | 1.166379e-16 | -1.638912e-15 | 1.311133e-15 | -3.675764e-17 | -7.075490e-17 | -2.447347e-16 |
| std | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 | 1.000171e+00 |
| min | -3.338221e+00 | -4.384322e+00 | -4.685578e+00 | -3.484829e+00 | -5.245552e-02 | -3.412454e+00 | -6.006751e+00 | -2.292982e+00 | -5.852905e+00 | -3.042174e+00 | -7.747908e+00 | -1.586949e+01 | -2.108119e+00 | -7.274572e+00 | -4.501270e+00 | -5.428998e+00 | -5.647789e+00 | -2.811691e+00 | -4.354505e+00 | -3.104923e+00 | -9.189028e-01 | -7.940465e-01 | -6.205154e-01 | -3.670969e-01 | -7.564916e+00 | -4.542077e+00 | -3.876230e+00 | -4.067862e+00 | -2.511079e+00 | -1.839246e+00 | -2.102006e+00 | -3.341489e+00 | -3.459809e+00 | -3.731950e+00 | -3.054151e+00 | -1.840744e+00 | -1.947584e+00 | -3.257909e+00 | -3.174584e+00 | -3.697490e+00 | -1.766382e+00 | -2.335709e+00 | -7.782794e-02 | -1.061078e+01 | -2.454902e+00 | -1.849001e-02 | -3.037851e+00 | -3.440714e+00 | -3.918944e+00 | -7.564633e+00 | -4.172798e+00 | -1.165962e+00 | -9.313965e+00 | -2.546414e+00 | -3.252697e+00 | -1.682567e+00 | -1.389307e+01 | -3.211206e+00 | -3.954851e+00 | -2.069502e+00 | -9.696125e-01 | -1.921131e+00 | -2.146855e+00 | -3.051629e+00 | -1.542735e+00 | -1.633106e+00 | -1.678345e+00 | -4.710887e-01 | -3.848262e-01 | -8.443901e-01 | -9.186053e-01 | -1.020348e+00 | -7.621587e-01 | -9.986183e-01 | -1.206156e+00 | -1.313092e+00 | -1.804513e+00 | -2.414957e+00 | -2.903425e+00 | -1.226636e+00 | -2.915744e+00 | -2.059514e+00 | -2.020780e+00 | -3.351246e+00 | -1.240546e+00 | -9.635108e-01 | -1.748800e+00 | -4.293068e-01 | -7.004797e-01 | -1.312075e+00 | -1.463924e+00 | -2.417641e+00 | -2.320950e+00 | -1.877159e+00 | -1.978939e+00 | -1.766472e+00 | -2.419749e+00 | -1.807824e+00 | -2.116715e+00 | -1.078279e+00 | -1.005759e+00 | -1.734599e+00 | -8.344158e-01 | -9.326917e-01 | -1.405784e+00 | -1.642950e+00 | -1.656651e+00 | -8.724692e-01 | -2.564855e+00 | -1.470216e+00 | -1.284634e+00 | -1.091019e+00 | -1.137218e+00 | -1.005309e+00 | -8.918423e-01 | -1.147405e+00 | -8.920867e-01 | -1.318982e+00 | -1.218706e+00 | -7.444346e-01 | -7.482532e-01 | -1.304467e+00 | -9.070010e-01 | -1.386918e-01 | -1.632930e+00 | -7.249625e+00 | -1.362611e+00 | -9.597239e-01 | -1.613378e+00 | -5.690974e-01 | -1.279630e+00 | -2.830657e+00 | -3.768481e+00 | -2.158284e+00 | -9.536343e+00 | -3.531461e+00 | -2.769371e-01 | -1.274936e+00 | -1.133444e+00 |
| 25% | -6.886170e-01 | -5.418254e-01 | -7.254718e-01 | -7.502949e-01 | -4.396021e-02 | -4.508966e-01 | -5.557568e-01 | -7.498768e-01 | -4.691685e-01 | -1.754332e-01 | -5.871694e-01 | -1.863474e-01 | -3.147996e-01 | -2.390558e-01 | -4.558051e-01 | -4.067933e-01 | 2.391383e-01 | -5.679729e-01 | -7.384097e-01 | -5.096001e-01 | -3.815183e-01 | -2.751577e-01 | -2.774890e-01 | -2.179227e-01 | 2.927340e-02 | -5.977723e-01 | -5.187869e-01 | -3.731964e-01 | 1.096162e-02 | -3.838134e-01 | -7.554844e-01 | -7.095340e-01 | -7.791092e-01 | -9.966976e-01 | -6.531232e-01 | -6.970810e-01 | -8.547846e-01 | -6.197928e-01 | -5.739915e-01 | -5.168353e-01 | -5.663905e-01 | -6.823527e-01 | -7.681758e-02 | -3.961743e-01 | -5.348570e-01 | -1.849001e-02 | -6.526672e-01 | -5.662601e-01 | -6.293830e-01 | -5.019078e-01 | -4.633043e-01 | -1.094815e-01 | -5.733761e-01 | -7.711466e-01 | -6.901167e-01 | -7.066072e-01 | -5.426677e-01 | -2.663841e-01 | -5.957160e-01 | -7.355657e-01 | -4.426491e-01 | -9.008100e-01 | -7.276373e-01 | -5.319717e-01 | -6.204448e-01 | -5.003332e-01 | -7.146051e-01 | -2.141055e-01 | -2.396272e-01 | -4.779399e-01 | -4.500038e-01 | -6.666802e-01 | -6.793756e-01 | -4.127780e-01 | -4.544233e-01 | -4.960570e-01 | -7.784124e-01 | -7.409014e-01 | -7.365970e-01 | -5.765170e-01 | -6.732134e-01 | -7.887363e-01 | -7.232686e-01 | -8.486846e-01 | -4.618885e-01 | -3.282711e-01 | -8.123687e-01 | -1.658063e-01 | -4.050311e-01 | -3.247731e-01 | -5.499764e-01 | -5.933315e-01 | -7.314829e-01 | -5.624802e-01 | -6.589478e-01 | -6.318598e-01 | -7.491819e-01 | -8.363218e-01 | -7.742550e-01 | -1.078279e+00 | -1.005759e+00 | -7.085071e-01 | -5.648404e-01 | -8.856361e-01 | -4.750652e-01 | -6.062059e-01 | -6.513860e-01 | -7.381417e-01 | -6.585759e-01 | -5.748665e-01 | -5.744166e-01 | -1.091019e+00 | -6.395150e-01 | -6.672101e-01 | -6.595630e-01 | -1.147405e+00 | -6.138900e-01 | -9.194146e-01 | -7.018020e-01 | -7.444346e-01 | -7.482532e-01 | -5.519460e-01 | -9.070010e-01 | -1.386918e-01 | -5.863198e-01 | -7.071125e-01 | -6.841979e-01 | -4.795198e-01 | -4.156441e-01 | -2.093393e-01 | -8.375412e-01 | -9.031733e-02 | -8.983833e-01 | -5.185329e-01 | 4.669435e-02 | -3.811488e-01 | -2.243963e-01 | -5.002600e-01 | -6.241772e-01 |
| 50% | -1.419291e-01 | 5.729133e-02 | -7.139848e-02 | -2.357250e-01 | -3.714983e-02 | 4.738815e-02 | -8.037606e-02 | 4.458867e-02 | -3.980294e-02 | -2.310509e-02 | -1.504068e-01 | 3.576423e-02 | 3.789857e-02 | -3.776817e-02 | -1.780168e-02 | 1.193549e-01 | 4.140209e-01 | -5.475853e-02 | -3.850667e-03 | -3.902394e-01 | -1.293088e-01 | -1.805298e-01 | -1.637235e-01 | -1.518329e-01 | 1.637159e-01 | 1.058072e-02 | -7.394574e-02 | -2.104048e-01 | 4.886535e-01 | -1.600970e-01 | -2.076568e-01 | -3.065071e-03 | -2.223488e-02 | 3.858905e-01 | 1.358678e-02 | 4.584553e-01 | -7.924980e-02 | -2.962081e-01 | 1.053626e-02 | 1.054975e-02 | -1.903345e-01 | -1.577344e-01 | -7.661549e-02 | 4.772029e-02 | -4.920217e-02 | -1.849001e-02 | -1.482858e-02 | -2.321794e-02 | -2.703940e-03 | -5.415619e-02 | 8.942053e-02 | 1.635143e-02 | 2.232213e-02 | -1.635618e-01 | -1.714758e-01 | -1.187258e-01 | 2.387019e-02 | 2.619898e-01 | -5.019843e-02 | -1.652009e-01 | -1.536691e-01 | -1.156624e-01 | 1.873640e-02 | -1.618789e-01 | -3.080484e-01 | -9.552838e-02 | -2.659927e-01 | -7.338577e-02 | -1.307279e-01 | -2.879909e-01 | -2.368693e-01 | -2.704324e-01 | -4.666815e-01 | -1.923005e-01 | -1.652951e-01 | -8.753947e-02 | 1.049937e-02 | -9.670188e-03 | -2.469002e-02 | -3.598107e-01 | -8.945362e-02 | -9.868414e-02 | -1.502973e-01 | 2.342863e-01 | -1.540473e-01 | -1.153214e-01 | -6.221787e-02 | -1.085990e-01 | -2.001682e-01 | -6.781610e-02 | -2.420351e-01 | 7.014277e-03 | -7.909889e-02 | -6.579635e-02 | -1.965544e-01 | -1.193301e-01 | -4.975227e-02 | -1.976003e-01 | -8.491326e-02 | -7.984399e-02 | -3.645515e-02 | -2.918481e-01 | -3.321822e-01 | -2.860058e-01 | -1.621979e-01 | -1.035702e-01 | -2.714184e-01 | -3.915407e-01 | -2.554387e-02 | -1.864888e-01 | -2.615605e-01 | -9.941606e-02 | -2.799258e-01 | -3.006166e-01 | -3.626011e-01 | -2.109935e-01 | -4.713088e-01 | 1.896001e-02 | -2.399886e-01 | -7.444346e-01 | -7.482532e-01 | -2.763789e-01 | -3.374926e-01 | -1.386918e-01 | -2.727156e-01 | -1.676684e-01 | -4.300992e-01 | -2.619593e-01 | -1.581741e-01 | -1.000457e-01 | -1.689690e-01 | 2.423364e-01 | -2.068432e-01 | -4.449282e-01 | 1.062042e-01 | 1.607569e-01 | -2.077516e-01 | -1.684677e-01 | -3.025251e-01 |
| 75% | 5.641467e-01 | 5.229921e-01 | 5.930277e-01 | 5.011071e-01 | -3.222893e-02 | 5.182499e-01 | 6.856686e-01 | 7.011072e-01 | 4.233169e-01 | 1.322284e-01 | 7.720682e-01 | 3.367680e-01 | 2.562893e-01 | 3.284730e-01 | 3.540635e-01 | 6.371434e-01 | 5.194232e-01 | 6.328238e-01 | 7.024854e-01 | -2.093535e-01 | 9.025109e-02 | 6.353720e-03 | -2.919851e-02 | -9.277414e-02 | 2.774813e-01 | 4.690846e-01 | 2.763457e-01 | 1.003045e-01 | 5.623859e-01 | 1.996561e-01 | 8.595941e-01 | 7.174258e-01 | 6.367453e-01 | 8.508604e-01 | 6.672873e-01 | 8.029509e-01 | 6.610334e-01 | 4.179721e-01 | 6.183012e-01 | 5.107206e-01 | 3.382889e-01 | 5.152500e-01 | -7.643957e-02 | 4.677844e-01 | 2.890000e-01 | -1.849001e-02 | 6.570854e-01 | 6.249166e-01 | 5.647721e-01 | 5.542846e-01 | 5.912832e-01 | 1.309247e-01 | 5.233398e-01 | 6.169227e-01 | 5.313003e-01 | 4.153625e-01 | 5.428362e-01 | 5.261303e-01 | 8.180875e-01 | 5.267891e-01 | 1.693085e-01 | 8.014232e-01 | 7.681319e-01 | 3.892995e-01 | 2.845114e-01 | 2.990713e-01 | 4.395709e-01 | 5.205284e-02 | 3.867095e-02 | 1.269154e-02 | 6.081959e-03 | 2.251252e-01 | 3.639563e-01 | 1.557832e-02 | 1.816586e-01 | 3.209781e-01 | 7.117543e-01 | 7.031748e-01 | 6.167817e-01 | 9.888428e-02 | 6.506134e-01 | 6.242836e-01 | 6.775543e-01 | 7.350699e-01 | 2.635089e-01 | 2.004938e-01 | 5.795831e-01 | -4.792457e-02 | 2.137386e-01 | 1.874164e-01 | 2.400769e-01 | 6.265793e-01 | 5.931050e-01 | 4.448246e-01 | 4.349262e-01 | 4.280940e-01 | 6.227256e-01 | 7.202231e-01 | 5.169109e-01 | 6.809268e-01 | 7.850633e-01 | 5.729527e-01 | 1.477770e-01 | 4.318204e-01 | 2.435137e-01 | 4.343347e-01 | 4.694652e-01 | 3.594144e-01 | 6.585850e-01 | 2.530110e-01 | 2.827508e-01 | 7.205077e-01 | 3.312635e-01 | 3.691622e-01 | 3.299544e-01 | 6.570853e-01 | 5.047076e-01 | 6.327560e-01 | 4.251902e-01 | 8.365717e-01 | 7.050832e-01 | 1.426466e-01 | 9.721619e-01 | -1.386918e-01 | 5.022600e-01 | 7.456235e-01 | 5.452726e-01 | 1.963577e-01 | 1.842895e-01 | -1.807553e-02 | 5.705649e-01 | 2.510022e-01 | 4.723480e-01 | 4.914880e-01 | 2.045177e-01 | 6.543207e-01 | -1.873971e-01 | 2.028236e-01 | 2.352557e-01 |
| max | 4.356449e+00 | 4.582089e+00 | 3.864230e+00 | 5.932152e+00 | 2.706863e+01 | 4.956723e+00 | 2.437031e+01 | 3.842126e+00 | 3.031353e+01 | 5.172143e+01 | 9.571230e+00 | 3.019100e+00 | 7.114757e+00 | 2.704520e+00 | 4.478573e+00 | 5.463870e+00 | 8.880947e-01 | 2.665991e+00 | 2.877685e+00 | 2.348775e+00 | 7.938115e+00 | 7.279888e+00 | 7.564915e+00 | 1.322262e+01 | 6.205074e-01 | 5.927726e+00 | 2.803591e+00 | 8.625779e+00 | 8.102523e-01 | 1.833026e+01 | 3.678059e+00 | 6.139754e+00 | 4.891519e+00 | 1.762115e+00 | 5.107343e+00 | 1.612531e+00 | 2.811380e+00 | 1.529686e+01 | 4.430823e+00 | 1.825759e+01 | 2.337051e+01 | 4.391881e+00 | 1.511682e+01 | 3.682250e+00 | 4.037195e+00 | 5.408327e+01 | 2.944863e+00 | 5.540444e+00 | 5.456301e+00 | 1.264295e+01 | 3.736544e+00 | 5.257469e+01 | 9.591365e+00 | 3.807047e+00 | 5.199461e+00 | 4.661172e+00 | 2.951703e+00 | 2.593741e+00 | 2.617459e+00 | 8.756731e+00 | 1.494128e+01 | 2.887897e+00 | 4.208905e+00 | 6.554609e+00 | 5.078330e+00 | 3.294539e+01 | 4.745362e+00 | 2.288400e+01 | 1.482477e+01 | 5.703806e+00 | 5.221436e+00 | 9.014579e+00 | 5.324178e+00 | 5.029867e+00 | 1.053245e+01 | 2.047451e+01 | 3.851932e+00 | 2.739832e+00 | 3.410417e+00 | 5.314282e+00 | 9.611167e+00 | 1.430424e+01 | 1.207037e+01 | 6.889493e+00 | 3.283076e+01 | 4.703498e+01 | 6.813646e+00 | 7.818950e+00 | 1.313404e+01 | 1.562381e+01 | 2.125549e+01 | 3.598155e+00 | 4.336380e+00 | 3.449155e+01 | 5.682970e+00 | 1.998597e+01 | 4.579806e+00 | 2.903561e+00 | 5.409498e+00 | 2.425420e+00 | 2.112177e+00 | 1.034063e+01 | 7.552133e+00 | 3.270419e+00 | 2.008285e+01 | 1.750937e+01 | 8.696775e+00 | 3.513452e+00 | 7.250938e+00 | 1.440685e+01 | 1.557880e+01 | 2.442253e+00 | 4.590719e+00 | 4.080271e+00 | 3.829736e+00 | 2.448057e+00 | 2.964948e+00 | 2.640371e+00 | 3.120455e+00 | 2.346523e+00 | 2.255183e+00 | 7.804406e+00 | 2.018111e+00 | 7.984870e+00 | 8.667629e+00 | 4.174997e+00 | 2.519895e+00 | 1.052950e+01 | 1.624879e+01 | 1.728674e+01 | 3.684222e+00 | 6.829999e+00 | 6.459521e+00 | 1.076753e+01 | 2.672819e+00 | 2.045460e+00 | 5.752829e+00 | 9.241693e+00 | 7.163063e+00 |
# LOGISTIC REGRESSION WITH BALANCEED TARGET SET
print('''\033[1m''' + '''\tLOGISTIC REGRESSION WITH BALANCEED TARGET SET''' + '''\033[0m''')
model = LogisticRegression()
model.fit(nx_train, ny_train)
model.fit(nx_test, ny_test)
ny_predict = model.predict(nx_test)
model_train = model.score(nx_train,ny_train)
model_test = model.score(nx_test,ny_test)
score_train['LOG-REG TRAIN'] = model_train
score_test['LOG-REG TEST'] = model_test
print('''\n\033[1m''' + '''LOG-REG TRAIN SCORE''' + '''\033[0m''',model_train)
print('''\n\033[1m''' + '''LOG-REG TEST SCORE''' + '''\033[0m''',model_test)
print('''\033[1m''' + '''\n\t\tLOG-REG MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',metrics.classification_report(ny_test, ny_predict))
print('''\n\n\033[1m''' + '''LOG-REG MODEL CONFUSION METRICS WITH HEATMAP\n''' + '''\033[0m''',metrics.confusion_matrix(ny_test, ny_predict))
cm1 = metrics.confusion_matrix(ny_test, ny_predict)
plt.figure(figsize = (5,3))
sns.heatmap(cm1, annot=True,cmap='Blues', fmt='g')
LOGISTIC REGRESSION WITH BALANCEED TARGET SET LOG-REG TRAIN SCORE 0.8193359375 LOG-REG TEST SCORE 0.907744874715262 LOG-REG MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 0.93 0.87 0.90 415 1.0 0.89 0.94 0.92 463 accuracy 0.91 878 macro avg 0.91 0.91 0.91 878 weighted avg 0.91 0.91 0.91 878 LOG-REG MODEL CONFUSION METRICS WITH HEATMAP [[361 54] [ 27 436]]
<AxesSubplot:>
# KFOLD CROSS VALIDATION
print('''\033[1m''' + '''KFOLD CROSS VALIDATION''' + '''\033[0m''')
print('''\033[1m''' + '''LOGISTIC REGRESSION MODEL''' + '''\033[0m''')
kfold = KFold(n_splits = 25, random_state = 56)
model_train= cross_val_score(model,nx_train,ny_train,cv = kfold)
model_test= cross_val_score(model,nx_test,ny_test,cv = kfold)
score_train['LOG-REG KFLOD TRAIN SCORE'] = model_train.mean()
score_test['LOG-REG KFLOD TEST SCORE'] = model_test.mean()
print('''\033[1m''' + '''KFOLD TRAIN OVERALL SCORE''' + '''\033[0m {:.2f}% WITH STD+/-({:.2f}%)'''.format(model_train.mean(),model_train.std()))
print('''\033[1m''' + '''KFOLD TEST OVERALL SCORE ''' + '''\033[0m {:.2f}% WITH STD+/-({:.2f}%)'''.format(model_test.mean(),model_test.std()))
KFOLD CROSS VALIDATION LOGISTIC REGRESSION MODEL KFOLD TRAIN OVERALL SCORE 0.83% WITH STD+/-(0.04%) KFOLD TEST OVERALL SCORE 0.83% WITH STD+/-(0.06%)
# LEAVE ONE OUT CROSS VALIDATION
print('''\033[1m''' + '''LEAVE ONE OUT CROSS CROSS VALIDATION''' + '''\033[0m''')
print('''\033[1m''' + '''LOGISTIC REGRESSION MODEL''' + '''\033[0m''')
loocv = LeaveOneOut()
model_train= cross_val_score(model,nx_train,ny_train,cv = loocv)
model_test= cross_val_score(model,nx_test,ny_test,cv = loocv)
score_train['LOG-REG LOOCV TRAIN SCORE'] = model_train.mean()
score_test['LOG-REG LOOCV TEST SCORE'] = model_test.mean()
print('''\033[1m''' + '''LOOCV TRAIN OVERALL ACURRACY ''' + '''\033[0m {:.2f}% WITH STD+/-({:.2f}%)'''.format(model_train.mean(),model_train.std()))
print('''\033[1m''' + '''LOOCV TEST OVERALL ACURRACY ''' + '''\033[0m {:.2f}% WITH STD+/-({:.2f}%)'''.format(model_test.mean(),model_test.std()))
LEAVE ONE OUT CROSS CROSS VALIDATION LOGISTIC REGRESSION MODEL LOOCV TRAIN OVERALL ACURRACY 0.83% WITH STD+/-(0.37%) LOOCV TEST OVERALL ACURRACY 0.84% WITH STD+/-(0.37%)
# LR HYPERPARAMETER TUNUNG
print('''\033[1m''' + '''HYPERPARAMETER TUNUNG''' + '''\033[0m''')
model = LogisticRegression()
solvers = ['newton-cg', 'lbfgs', 'liblinear']
penalty = ['l2']
c_values = [100, 10, 1.0, 0.1, 0.01, 0.001]
# define grid search
grid = dict(solver=solvers, penalty=penalty, C=c_values)
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
randomCV = RandomizedSearchCV(estimator=model, param_distributions=grid, n_jobs=-1, cv=cv,
scoring='accuracy',error_score=0, iid=True)
grid_result = randomCV.fit(nx,ny)
# summarize results
print(f"Best model score of Random Forest using parameters {grid_result.best_params_} is {grid_result.best_score_:.3f}")
HYPERPARAMETER TUNUNG
Best model score of Random Forest using parameters {'solver': 'newton-cg', 'penalty': 'l2', 'C': 100} is 0.844
# APPLIED LR HYPERPARAMETER TUNING
print('''\033[1m''' + '''\tLOGISTIC REGRESSION HYPERPARAMETER TUNING''' + '''\033[0m''')
model = LogisticRegression( C = 100, penalty = 'l2' , solver = 'newton-cg')
model.fit(nx_train, ny_train)
model.fit(nx_test, ny_test)
ny_predict = model.predict(nx_test)
model_train = model.score(nx_train,ny_train)
model_test = model.score(nx_test,ny_test)
score_train['LOG-REG TUINING TRAIN'] = model_train
score_test['LOG-REG TUINING TEST'] = model_test
print('''\n\033[1m''' + '''LOG-REG TUINING TRAIN SCORE''' + '''\033[0m''',model_train)
print('''\n\033[1m''' + '''LOG-REG TUINING TEST SCORE''' + '''\033[0m''',model_test)
print('''\033[1m''' + '''\n\t\tLOG-REG TUINING MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',metrics.classification_report(ny_test, ny_predict))
print('''\n\n\033[1m''' + '''LOG-REG TUINING MODEL CONFUSION METRICS WITH HEATMAP\n''' + '''\033[0m''',metrics.confusion_matrix(ny_test, ny_predict))
cm2 = metrics.confusion_matrix(ny_test, ny_predict)
plt.figure(figsize = (5,3))
sns.heatmap(cm2, annot=True,cmap='Blues', fmt='g')
LOGISTIC REGRESSION HYPERPARAMETER TUNING LOG-REG TUINING TRAIN SCORE 0.8173828125 LOG-REG TUINING TEST SCORE 0.908883826879271 LOG-REG TUINING MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 0.93 0.87 0.90 415 1.0 0.89 0.94 0.92 463 accuracy 0.91 878 macro avg 0.91 0.91 0.91 878 weighted avg 0.91 0.91 0.91 878 LOG-REG TUINING MODEL CONFUSION METRICS WITH HEATMAP [[362 53] [ 27 436]]
<AxesSubplot:>
def SampleNDrop(df):
global sample_df
sample_df = df.sample(n = 200)
# sample2_df = sig_df.sample(n =100)
x_diff = sample_df.drop(['Pass/Fail'],axis=1)
y_diff = sample_df['Pass/Fail']
return x_diff,y_diff
x_diff_n, y_diff_n = SampleNDrop(sd1)
#Creating a user defined function to split the new sample
def SplitTrainTest(par_X,par_y):
# tr_size = 0.7
# test_size = 0.3
# rs = 40
X_train_,X_test_,y_train_,y_test_ = train_test_split(par_X,par_y,train_size=0.7, test_size=0.3, random_state=40)
return X_train_,X_test_,y_train_,y_test_
X_train_d,X_test_d,y_train_d,y_test_d = SplitTrainTest(x_diff_n, y_diff_n)
#Creating a user defined function to find the train and test accuracies of the new sample
def FindAccuracy(tr_x,tr_y,ts_x,ts_y):
achieved_score = []
achieved_score.append(pipe.score(tr_x,tr_y))
achieved_score.append(pipe.score(ts_x,ts_y))
return achieved_score
pipe= make_pipeline(StandardScaler(),model)
pipe.fit(X_train_d, y_train_d)
pipe.fit(X_test_d, y_test_d)
Pipeline(steps=[('standardscaler', StandardScaler()),
('logisticregression',
LogisticRegression(C=100, solver='newton-cg'))])
Checking the achieved train and test accuracies with the different sample population
print('''\n\033[1m''' + '''Checking the achieved test accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved test accuracy of kNN"
Ha = "There is a change in the achieved test accuracy of kNN"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved test accuracies with the different sample population
No change in the achieved test accuracy of kNN as the p_value (nan) > 0.05
print('''\n\033[1m''' + '''Checking the achieved train accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved train accuracy of kNN"
Ha = "There is a change in the achieved train accuracy of kNN"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved train accuracies with the different sample population
No change in the achieved train accuracy of kNN as the p_value (nan) > 0.05
sd1=sd.drop('Time',axis=1)
sd2=sd1
print('''\n\033[1m''' + '''Replacing other columns by their median value:''' + '''\033[0m''')
for x in sd2:
s = sd2[x].isnull().sum()
if s!=0:
median=sd1[x].median()
#print('Median of',x,median)
sd2[x].fillna(median,inplace=True)
Replacing other columns by their median value:
print('''\n\033[1m''' + '''Droping all columns, of NaN correlaton with Pass/Fail column''' + '''\033[0m''')
stdo = []
for x in sd2:
sd2[x]=sd2[x].astype(float)
att = sd1[x]
std = np.std(att)
if std == 0:
stdo.append(x)
sd2=sd2.drop(x,axis=1)
print('Which are:',stdo)
Droping all columns, of NaN correlaton with Pass/Fail column
Which are: ['5', '13', '42', '49', '52', '69', '97', '141', '149', '178', '179', '186', '189', '190', '191', '192', '193', '194', '226', '229', '230', '231', '232', '233', '234', '235', '236', '237', '240', '241', '242', '243', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '276', '284', '313', '314', '315', '322', '325', '326', '327', '328', '329', '330', '364', '369', '370', '371', '372', '373', '374', '375', '378', '379', '380', '381', '394', '395', '396', '397', '398', '399', '400', '401', '402', '403', '404', '414', '422', '449', '450', '451', '458', '461', '462', '463', '464', '465', '466', '481', '498', '501', '502', '503', '504', '505', '506', '507', '508', '509', '512', '513', '514', '515', '528', '529', '530', '531', '532', '533', '534', '535', '536', '537', '538']
sd2=sd2.apply(zscore)
x=sd2.drop('Pass/Fail',axis=1)
y=sd2['Pass/Fail']
x
| 0 | 1 | 2 | 3 | 4 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 43 | 44 | 45 | 46 | 47 | 48 | 50 | 51 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 70 | 71 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 180 | 181 | 182 | 183 | 184 | 185 | 187 | 188 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 221 | 222 | 223 | 224 | 225 | 227 | 228 | 238 | 239 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 316 | 317 | 318 | 319 | 320 | 321 | 323 | 324 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 359 | 360 | 361 | 362 | 363 | 365 | 366 | 367 | 368 | 376 | 377 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 452 | 453 | 454 | 455 | 456 | 457 | 459 | 460 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 493 | 494 | 495 | 496 | 497 | 499 | 500 | 510 | 511 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 539 | 540 | 541 | 542 | 543 | 544 | 545 | 546 | 547 | 548 | 549 | 550 | 551 | 552 | 553 | 554 | 555 | 556 | 557 | 558 | 559 | 560 | 561 | 562 | 563 | 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.224463 | 0.849523 | -0.436430 | 0.035804 | -0.050121 | -0.564354 | 0.265894 | 0.509848 | 1.128455 | -0.381577 | -1.515617 | 0.763117 | -0.375756 | 0.103879 | 0.056566 | -0.286055 | 0.845957 | 0.174249 | -0.146683 | 0.318209 | 0.735614 | -0.172197 | 0.361844 | -1.738184 | -0.874376 | -2.887917 | -1.522835 | -0.842542 | -0.636905 | -0.287505 | -0.958019 | 0.411164 | 0.029174 | -0.115322 | -0.029182 | 0.305382 | 0.266386 | -0.645694 | -0.283379 | 0.498003 | -0.452655 | 0.874271 | -0.812538 | -0.852081 | 0.681680 | 0.277780 | -0.919041 | 0.999838 | -0.017844 | 0.041453 | -0.862229 | 0.418572 | -0.195538 | 1.323374 | -0.491928 | -0.702824 | 0.728880 | -0.912747 | 0.303850 | 0.239003 | 0.339085 | -1.111942 | -0.051277 | 0.274832 | -1.148517 | -0.640985 | -0.02527 | 0.881524 | 0.266656 | 0.680780 | -0.359555 | -0.511608 | 0.323056 | -2.106521 | -0.292813 | -0.457000 | -0.220082 | -0.331499 | -1.044890 | -1.124999 | -0.090271 | -0.399141 | -3.770051 | -1.860871 | 0.146352 | 0.742635 | 2.304328 | -1.349572 | 0.974932 | -0.425465 | 0.623511 | 0.940718 | 2.131088 | 1.825649 | 0.370180 | 1.908082 | -0.180534 | -2.807550 | 3.816726 | 0.079873 | 0.047474 | -0.073477 | 0.025089 | 0.386589 | -0.030126 | 0.221393 | 1.075702 | 0.546711 | 0.842009 | -1.397272 | 1.021946 | 1.189792 | -1.141040 | 2.384047 | -1.768518 | -0.015944 | 0.414601 | -0.252273 | -0.805928 | -0.411341 | -0.508802 | -0.051032 | 0.088230 | -0.499742 | -0.910786 | -0.926805 | -0.249510 | -0.050451 | 0.040121 | -0.600920 | -0.492646 | 0.806501 | -0.879920 | 0.567994 | 0.202214 | -0.784953 | -0.121355 | -0.030939 | 0.511593 | 0.801234 | -0.085803 | -0.094613 | 0.137099 | 0.717086 | -0.708061 | -0.675808 | -0.411725 | -0.329719 | -0.385650 | -0.704959 | -0.531093 | -1.163815 | -1.792703 | 0.258431 | -0.101393 | -0.025491 | 0.131773 | -0.024089 | 1.478412 | 2.045992 | -0.830108 | 0.585265 | -0.953163 | 0.410881 | -1.456217 | -0.585753 | -0.229023 | -0.784044 | 1.020986 | 0.012640 | -0.210440 | 0.000955 | -0.486144 | -0.054889 | -0.841960 | -0.482042 | -0.419775 | -0.794549 | -0.056399 | -0.035179 | -0.02527 | -0.577007 | 0.858785 | -0.02527 | -0.276105 | 0.128966 | 0.597120 | -0.106060 | -0.227593 | -0.963695 | -2.089364 | 0.914821 | -0.250044 | -1.373707 | 0.428472 | 0.219582 | 0.949805 | -0.388463 | 2.116156 | 5.144168 | 3.424208 | -1.232734 | -0.399062 | -0.281132 | -0.129445 | -0.067565 | 2.020788 | -0.085018 | -0.028099 | -0.641834 | -0.979624 | 0.191627 | -0.791079 | 2.916146 | 0.642033 | 0.205434 | -0.678459 | -1.039246 | -1.107771 | -0.303274 | -0.050465 | 0.318029 | -0.346240 | -0.451498 | 0.257514 | -0.558185 | 0.600637 | 0.192585 | -0.631168 | -0.069446 | -0.031638 | 0.537776 | 0.784035 | -0.057288 | -0.126317 | 0.034575 | 0.513271 | -0.700430 | -0.676908 | -0.412851 | -0.306353 | -0.329499 | -0.909892 | -0.507350 | -1.403195 | -1.769648 | 0.310084 | -0.205669 | 0.192301 | 0.699549 | 0.195915 | 1.433257 | 1.589957 | -0.844610 | 0.287741 | -0.753640 | 0.520266 | -1.167579 | -0.555565 | -0.301756 | -0.853058 | 0.707864 | -0.126902 | -0.176208 | 0.110233 | -0.445849 | -0.057607 | -0.168241 | -0.429074 | -0.320769 | -0.658114 | -0.055641 | 0.033982 | -0.02527 | -0.597159 | 0.976545 | -0.02527 | -0.269615 | 0.205995 | 0.588231 | -0.153861 | -0.209531 | -1.076802 | -2.125201 | 0.661139 | -0.071898 | -1.198768 | 0.428934 | 0.177791 | 0.895409 | -0.360974 | 1.667683 | 4.447710 | 4.195657 | 3.203869 | 3.915044 | -1.125490 | -0.580658 | -0.232199 | -0.179601 | -0.068016 | 1.535180 | -0.087905 | -0.021416 | -0.410571 | -0.958773 | 0.252902 | -0.851226 | 2.662305 | 0.569658 | 0.108501 | -0.512152 | -0.942375 | -0.917673 | -0.328325 | 0.073534 | 0.081033 | -0.644611 | -0.544418 | 0.712732 | -0.950407 | 0.537868 | 0.272203 | -0.735988 | -0.132243 | -0.089474 | 0.496633 | 0.698334 | -0.088238 | -0.087582 | 0.009855 | 0.297172 | -0.578641 | -0.693567 | -0.137275 | -0.193877 | -0.127194 | -0.560884 | -0.298990 | -0.739794 | -1.658161 | 0.365113 | -0.224214 | -0.035879 | 0.136739 | -0.016240 | 1.462140 | 2.033503 | -0.830117 | 0.645357 | -0.974058 | 0.555004 | -1.414080 | -0.760863 | -0.267520 | -0.777840 | 0.055821 | -0.005236 | 0.381334 | 0.021031 | -0.518080 | 0.014282 | -1.557322 | -0.653556 | -0.966730 | -0.738187 | 0.796401 | -0.043280 | -0.02527 | -0.603717 | 1.127496 | 1.059160 | 0.448214 | 1.325827 | -0.099377 | 1.898791 | -0.905860 | -1.197654 | -1.199061 | -0.174260 | -1.380622 | 0.438514 | 0.184098 | 0.987510 | -0.515104 | 2.170517 | -0.809806 | -0.745562 | 0.236833 | -0.836511 | -0.266711 | -0.124479 | -0.112621 | 2.062967 | -0.083086 | -0.040267 | -0.620486 | -0.978015 | 0.157239 | -0.782303 | 2.932521 | 0.656685 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | 0.120805 | -1.709169 | 0.106900 | -0.336030 | -0.874533 | -0.351836 | -0.318719 | -0.959499 | -0.298562 | -0.538278 | -0.789264 | -0.338360 | 0.470186 | 0.560524 | 0.610426 | 0.531416 | 0.183094 | -0.212815 | -0.434630 | -0.289437 | -0.398435 | -0.314028 | -0.405707 | -0.362419 | 0.190142 | 0.034410 | -0.226018 | -0.120518 | -0.226665 | -0.031418 | -0.229797 | -0.135520 | 0.118679 | -0.204833 | -0.093165 | -0.197057 | -0.077554 | -0.190165 | -0.238334 | -0.295753 |
| 1 | 1.107287 | -0.383106 | 1.016977 | 0.155282 | -0.059585 | 0.197639 | 0.321868 | 0.457021 | 0.022620 | -1.608281 | -0.133006 | 0.181528 | 0.411562 | 0.095954 | -0.269742 | -0.111740 | 0.446647 | -0.093702 | -1.348004 | 0.282279 | -0.322078 | 0.223063 | -0.462748 | 0.526079 | 0.400176 | 0.603714 | -0.311431 | -0.243722 | 0.791123 | -0.478202 | -0.213375 | 0.624843 | 0.065219 | -0.105995 | -0.065226 | -4.283580 | 1.539930 | -0.310382 | 0.426118 | -0.245879 | -0.528535 | 0.035059 | -0.455129 | -0.727337 | 0.584067 | 1.298778 | -0.114815 | 0.783909 | -0.054299 | 0.058263 | -0.123172 | 0.521553 | -0.315462 | 1.044437 | -0.225470 | -0.523124 | -0.415640 | -0.292833 | -0.434113 | -0.271323 | 0.070691 | -0.816696 | -0.050504 | 1.638696 | 0.132044 | -0.695761 | -0.02527 | 0.135382 | 0.290945 | 0.240535 | -0.639619 | -1.712613 | 0.130617 | -0.981320 | -0.122354 | -1.255571 | 0.535338 | -0.709313 | 0.543432 | 2.313733 | -0.026190 | -1.078926 | 1.635323 | -0.621687 | 0.179608 | -2.710508 | 0.385687 | 2.594036 | -2.152325 | -0.276903 | -1.067849 | -1.783222 | -1.214612 | 1.727662 | 0.252518 | 0.624916 | -0.180534 | 0.904673 | -0.918512 | 0.079873 | -0.241090 | -0.073477 | -0.329673 | 0.323387 | 0.006493 | -0.326078 | 0.259668 | 1.571137 | 0.842009 | -1.506012 | 0.744088 | 0.750364 | -1.124234 | 0.080589 | -1.738883 | 0.303047 | 0.375734 | 0.816107 | 0.308458 | -0.477342 | -0.910434 | -0.492334 | -0.347032 | -1.076061 | -0.799505 | -0.112948 | -0.748877 | -0.050777 | -0.265361 | 0.572904 | 0.254674 | -0.136334 | -0.013913 | 0.110379 | 0.206236 | -0.162949 | -0.031152 | -0.026945 | 0.265177 | 0.792194 | -0.139442 | -0.181791 | -0.320064 | -0.864042 | -0.889622 | -0.231562 | -0.231211 | -0.053209 | -0.314387 | -0.526181 | -0.214719 | -0.953702 | 0.811879 | 2.322390 | 1.179342 | 1.283542 | 0.857162 | 1.283469 | 2.065088 | -0.176527 | -0.246518 | -0.309123 | -0.480496 | -0.153830 | -1.508181 | -0.504938 | -0.061989 | -1.032754 | 1.833175 | -0.164773 | -0.315023 | -0.047484 | -0.448869 | 0.007879 | -1.104980 | -0.804119 | -0.445896 | -0.860670 | -0.056288 | -0.007390 | -0.02527 | -0.707603 | 2.177721 | -0.02527 | -0.769603 | -0.318457 | 3.630806 | 1.853734 | -0.648933 | 0.582992 | -0.395199 | 0.070038 | 0.159379 | 1.066190 | 0.660254 | -0.132713 | 0.101235 | -0.498297 | 2.227862 | 0.109652 | 0.536435 | 0.948439 | -0.676833 | -0.281132 | -0.198524 | -0.067565 | 1.527628 | -0.095696 | -0.011583 | -0.650824 | -0.585286 | 1.421928 | -0.997415 | 1.398961 | -0.126007 | -0.023723 | -0.957504 | -1.072560 | -0.727890 | -0.616073 | -0.050788 | -0.305439 | 0.243818 | 0.145438 | -0.143911 | 0.055496 | 0.132373 | 0.187294 | -0.018432 | -0.031790 | -0.027232 | 0.333164 | 0.746201 | -0.085801 | -0.235946 | -0.351797 | -0.799029 | -0.881314 | -0.251566 | -0.211806 | -0.031683 | -0.270579 | -0.286378 | -0.288423 | -0.775471 | 0.773344 | 2.031070 | 0.901331 | 1.376267 | 1.328053 | 1.376132 | 1.622057 | -0.408111 | -0.329449 | -0.318948 | -0.427432 | -0.160212 | -1.226908 | -0.524183 | -0.217006 | -1.043742 | 1.556547 | -0.085843 | -0.255823 | -0.085475 | -0.466138 | -0.010925 | -0.264208 | -0.693529 | -0.339026 | -0.712527 | -0.055585 | -0.082774 | -0.02527 | -0.691957 | 2.083400 | -0.02527 | -0.889819 | -0.212280 | 3.773616 | 1.861092 | -0.692184 | 0.711622 | -0.158558 | 0.091958 | -0.132414 | 1.406877 | 0.695261 | -0.122266 | 0.375869 | -0.529929 | 2.523981 | 0.038527 | 0.456535 | 1.607301 | 0.061749 | 1.122382 | -0.580658 | -0.232199 | -0.218352 | -0.068016 | 1.738803 | -0.096371 | -0.013109 | -0.620014 | -0.620319 | 1.513442 | -1.141015 | 1.366822 | -0.549236 | -0.385926 | -1.120315 | -0.775723 | -0.174503 | -0.864877 | 0.046334 | -0.268316 | 0.510199 | 0.214902 | -1.114137 | 0.184403 | 0.097737 | 0.285913 | -0.483883 | -0.025506 | -0.029000 | 0.253461 | 0.701988 | -0.132235 | -0.157359 | -0.221336 | -0.552380 | -0.720404 | -0.025106 | -0.178849 | -0.106450 | -0.168842 | -0.497707 | -0.180904 | -0.862235 | 0.793380 | 2.359190 | 0.927465 | 1.243218 | 0.829045 | 1.324064 | 2.234098 | -0.201306 | -0.241890 | -0.260793 | -0.483300 | -0.088055 | -1.471449 | -0.665182 | -0.114351 | -1.044662 | 0.844309 | -0.174256 | 1.040027 | -0.030644 | -0.462016 | 0.058203 | -1.352284 | -0.889067 | -0.984289 | -0.806010 | 0.720941 | -0.044264 | -0.02527 | -0.755955 | 2.452948 | -1.140828 | 0.214695 | -1.011760 | 0.733318 | -0.599946 | 2.224655 | -0.632402 | -1.199061 | 0.377793 | 1.017787 | 0.691072 | -0.122839 | -0.048827 | -0.698259 | 2.431221 | -0.809806 | -0.745562 | 2.275389 | -0.836511 | -0.266711 | -0.187140 | -0.112621 | 1.642898 | -0.093240 | 0.046460 | -0.622954 | -0.584746 | 1.292987 | -0.995221 | 1.421923 | -0.010821 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | 0.932762 | 1.150527 | -0.245923 | 0.142104 | -0.198529 | 0.090654 | 0.075961 | -0.268656 | -0.001188 | -0.124810 | -0.237757 | 0.102057 | -0.376990 | -0.751178 | -0.677744 | -0.745482 | 0.183094 | -0.212815 | -0.434630 | -0.289437 | -0.398435 | -0.314028 | -0.405707 | -0.362419 | 0.256816 | 1.205944 | -0.261137 | -0.323417 | -0.265730 | -0.406218 | -0.263493 | -0.460054 | 0.530183 | 0.406734 | 0.444748 | 0.385113 | -0.960123 | 0.411970 | 0.250272 | 1.156846 |
| 2 | -1.114000 | 0.798901 | -0.481447 | 0.688278 | -0.047447 | -0.906768 | 0.254699 | -0.260885 | 0.327222 | 0.124169 | -0.229467 | 0.633530 | 0.182742 | 0.210657 | -0.247041 | -0.335860 | 0.956559 | 0.272774 | 0.433058 | 0.272299 | 0.008186 | -0.174554 | -0.558009 | 0.610045 | 0.480463 | 0.755573 | -0.684173 | -0.080607 | -0.326993 | -0.138873 | -0.286600 | -0.224141 | -0.363423 | -0.172607 | 0.363415 | -1.258050 | -4.712218 | -0.036862 | -2.246208 | 0.890441 | 1.418810 | -0.873898 | -0.629779 | 0.092064 | 0.639469 | 0.245998 | 0.580178 | 0.465101 | -1.949961 | -1.521941 | 3.105340 | -2.200093 | -1.082980 | -0.070136 | 2.195150 | 1.559854 | -0.928650 | -0.101503 | -0.378029 | -0.879033 | -0.400497 | 0.331273 | -0.051450 | -0.387222 | 0.720979 | -0.619174 | -0.02527 | -0.040979 | -0.097671 | 0.060580 | 0.735434 | -0.384957 | -1.945266 | 0.173491 | 0.632133 | -0.674475 | 0.614856 | 1.375363 | 0.434430 | -2.278834 | -0.758269 | 1.256667 | 0.604312 | 0.028884 | -0.252720 | 0.167111 | 1.345008 | -0.690935 | -0.292444 | 1.921173 | 1.751085 | -0.421252 | 0.195611 | 2.348246 | -1.865393 | -0.032315 | 0.255517 | -0.324715 | -0.716348 | 0.233348 | -1.840563 | -0.073477 | -0.588677 | 0.291787 | -0.022382 | 0.383145 | 0.717988 | 1.141410 | 1.043418 | -1.127641 | 1.021946 | 1.365563 | -1.090267 | 1.358969 | -1.265468 | 0.303047 | 0.298001 | 0.903619 | -0.003570 | -1.118492 | -0.858907 | 0.894818 | -0.503727 | -0.219019 | -0.502118 | -1.016321 | -0.419776 | -0.050587 | 0.478545 | -0.287900 | -0.727611 | -0.577581 | 0.315995 | 0.173117 | -0.010928 | -0.615708 | -0.180813 | -0.030189 | 0.618730 | 0.097044 | -0.032163 | -0.105984 | -0.326173 | 0.405388 | -0.780544 | -0.411307 | 0.384177 | -0.044955 | -0.451997 | -0.615570 | 0.259843 | -0.029204 | -0.412022 | 2.529548 | -1.368959 | -0.084482 | -0.009332 | -0.084481 | 1.631533 | 0.345711 | -0.242981 | -0.372576 | 0.883997 | 0.607929 | -0.548837 | 0.463937 | 0.256768 | -0.007699 | 0.778891 | -0.375135 | -0.228933 | -0.032287 | 0.730855 | -0.110343 | -0.100197 | -0.538995 | 0.061440 | -0.427558 | -0.056716 | 0.369491 | -0.02527 | -0.208705 | 0.226884 | -0.02527 | -1.313409 | -0.294272 | 0.584360 | -0.374083 | -0.768550 | 0.096134 | -1.901558 | 0.006897 | -1.149681 | -1.129717 | 1.527289 | 0.114073 | 1.081806 | -0.432682 | -0.467131 | 0.007287 | -0.078381 | 0.830538 | -0.399062 | 2.579257 | 0.043251 | -0.067565 | -0.096130 | -0.095696 | -0.007780 | 1.803386 | -0.837224 | 0.106004 | -0.740340 | 1.382304 | 0.118199 | -0.680630 | -0.262050 | -0.489862 | -1.155221 | -0.453778 | -0.050580 | 0.266885 | 0.243818 | -0.796832 | -0.437260 | 0.109644 | 0.202613 | 0.011385 | -0.612140 | -0.175256 | -0.029713 | 0.674184 | 0.139982 | -0.017296 | -0.126317 | -0.315148 | 0.270821 | -0.787130 | -0.446121 | 0.252625 | -0.014850 | -0.391616 | -0.644265 | -0.001082 | 0.086787 | -0.431800 | 2.086867 | -1.320547 | -0.004419 | 0.249766 | -0.004431 | 1.449374 | 0.700382 | -0.289362 | -0.247729 | 1.321097 | 0.547669 | -0.797464 | 0.360814 | 0.143876 | -0.312548 | 0.405156 | -0.930714 | -0.185230 | -0.079409 | 0.634651 | -0.045663 | -0.099235 | -0.459041 | -0.009066 | -0.511755 | -0.055837 | 0.282959 | -0.02527 | -0.093188 | 0.177882 | -0.02527 | -1.204169 | -0.421417 | 0.672612 | -0.428814 | -0.899036 | 0.158836 | -2.048078 | 0.173269 | -1.344811 | -0.998334 | 1.452192 | 0.082773 | 1.062767 | -0.421985 | -0.358082 | 0.076212 | -0.215666 | -0.286302 | 0.252035 | 0.935059 | -0.151565 | 1.179955 | 0.111037 | -0.068016 | -0.372888 | -0.087905 | -0.009659 | 1.205134 | -0.857237 | 0.091427 | -0.486652 | 1.686946 | -0.143640 | -0.490189 | -0.152229 | -0.547256 | -1.007130 | -0.700765 | -0.084075 | 0.532296 | -0.331785 | -0.732409 | -1.114137 | -0.950407 | 0.154926 | -0.011948 | -0.829408 | -0.203903 | -0.065512 | 0.596497 | 0.065769 | -0.043608 | -0.101012 | -0.224725 | 0.187442 | -0.638979 | -0.425033 | -0.014023 | -0.104828 | -0.191455 | -0.551876 | 0.155714 | 0.007272 | -0.431993 | 2.580271 | -1.394717 | -0.053305 | 0.014492 | -0.120376 | 1.674585 | 0.434070 | -0.243161 | -0.508667 | 0.925136 | 0.724378 | -0.552893 | 0.339124 | 0.278870 | -0.025414 | 0.237409 | -0.371878 | -0.879464 | -0.077517 | 0.798321 | -0.121402 | 0.213104 | -0.354262 | 0.320124 | -0.413930 | -0.451016 | 0.282222 | -0.02527 | -0.231820 | 0.515948 | 0.416929 | -0.281560 | -1.011760 | -0.043159 | 1.561412 | -0.623339 | -0.643117 | -0.093625 | -0.953304 | -1.158939 | 1.431491 | 0.087593 | 1.232679 | -0.438207 | -0.570085 | -0.809806 | -0.745562 | 4.913421 | -0.095153 | 2.318787 | 0.046768 | -0.112621 | -0.028099 | -0.091359 | 0.067736 | 1.704469 | -0.833253 | 0.032352 | -0.704821 | 1.403839 | -0.106040 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | -0.625302 | 1.655967 | -0.241481 | -0.724161 | -0.163297 | -0.295519 | -0.700794 | -0.247552 | -0.227419 | -1.060978 | -0.223482 | -0.274599 | 6.050806 | 0.229121 | 0.986789 | -0.396072 | 0.584471 | 2.056521 | -2.097595 | 6.467174 | -2.220023 | 6.587355 | -2.128830 | 5.149517 | 0.257279 | -0.263745 | -0.199823 | -0.633805 | -0.188395 | -0.600996 | -0.205046 | -0.590505 | -1.262799 | 0.022320 | 0.014418 | 0.029888 | 2.991195 | 3.627143 | 3.321511 | -0.178955 |
| 3 | -0.350156 | -0.199072 | -0.051705 | -1.104376 | -0.050831 | 0.502662 | -0.013974 | 0.343240 | -0.765369 | -0.370817 | -0.116929 | 0.581382 | 0.214786 | 0.535203 | -0.089594 | -0.227950 | 0.759334 | -0.013593 | -0.236334 | 0.239564 | -0.173036 | -0.513969 | -0.468610 | 0.618498 | 0.344715 | 0.542987 | -1.898787 | 0.681327 | 0.696934 | -0.545133 | -0.210758 | -0.209330 | -0.061360 | -0.172179 | 0.061352 | -3.376184 | -0.335669 | -0.905372 | 0.369643 | -0.883858 | 1.202003 | -0.575486 | -0.629779 | 0.016866 | 0.657936 | 0.568511 | 0.592588 | 0.533265 | -1.949961 | -1.521941 | 3.105340 | -2.200093 | -1.082980 | -0.070136 | 2.253475 | 1.048099 | -0.772960 | -0.037099 | -0.060112 | -0.990738 | -0.514423 | 0.322201 | -0.051103 | 0.051130 | 0.650680 | -1.079548 | -0.02527 | -2.198007 | -0.507540 | -1.057708 | 1.129208 | 2.803165 | -1.775346 | 1.417134 | 0.604189 | 0.272180 | -0.577913 | -0.039430 | 0.621291 | -1.041069 | 0.136926 | -1.595576 | 0.768908 | -0.249932 | -0.518768 | -0.408413 | 1.345008 | 0.109654 | 0.083944 | -0.336009 | -0.504062 | 0.032738 | -1.130254 | 1.270390 | 0.370180 | 0.249355 | 0.523856 | -0.533241 | -1.238507 | 0.131031 | 0.904923 | -0.073477 | -0.782633 | 0.365522 | 0.005798 | 0.395588 | 0.595024 | 1.469741 | -2.481235 | -0.850243 | -2.034494 | -1.798317 | 0.648179 | -1.071141 | -0.969862 | -0.277972 | 0.686669 | -0.251058 | -0.761352 | 1.163249 | -3.619035 | 0.574438 | 0.157872 | -0.375183 | 0.019750 | -0.796588 | -0.967602 | -0.050854 | -0.319103 | -0.913940 | -0.097774 | -0.917001 | 0.288502 | 0.058713 | 0.256080 | 0.698097 | 0.038738 | 0.004610 | 0.329459 | 0.817230 | -0.139442 | 0.149230 | -0.023774 | -0.348608 | -0.149214 | -0.273253 | -0.592239 | -0.082098 | -0.373363 | -0.973127 | -0.531093 | -0.806623 | -0.806502 | 1.615764 | -1.390359 | -0.819057 | -1.432934 | -0.819016 | 0.676679 | 0.006457 | -0.016619 | -0.843942 | 0.652122 | 0.934740 | -0.600068 | 0.306847 | -0.233199 | 0.030654 | 0.431442 | -0.375135 | -0.176004 | 0.333381 | 1.317921 | -0.054279 | -0.465887 | -0.375992 | 0.083585 | -0.330214 | -0.056310 | 0.044713 | -0.02527 | 0.091436 | 1.131457 | -0.02527 | 0.864211 | -0.419228 | 2.265488 | -0.472928 | -0.220452 | -0.188695 | -0.751248 | 0.472834 | 1.511869 | -0.580741 | 0.673130 | -0.130925 | 0.530337 | -0.020448 | -0.512190 | -0.364951 | -0.274005 | 1.125291 | -0.399062 | -0.436012 | -0.035986 | -0.067565 | -0.285006 | -0.087687 | -0.022278 | 0.333557 | -0.738639 | 0.144244 | -0.652394 | 1.683911 | -0.506155 | 0.552980 | -0.460407 | 0.175113 | -0.850145 | -1.005496 | -0.050899 | -0.026961 | -1.231327 | 0.031971 | -1.008518 | 0.380386 | 0.062134 | 0.240941 | 0.807560 | -0.021094 | 0.002861 | 0.333164 | 0.843233 | -0.116906 | 0.179078 | 0.029053 | -0.338329 | -0.154900 | -0.298812 | -0.549999 | -0.120434 | -0.328129 | -0.842957 | -0.500255 | -0.809961 | -0.766648 | 1.453921 | -1.411156 | -1.039023 | -1.439152 | -1.038942 | 0.961256 | -0.001548 | 0.063745 | -0.523469 | 0.383941 | 0.824090 | -0.727115 | 0.150549 | -0.250377 | -0.169813 | 0.363881 | -0.930714 | -0.152624 | 0.121099 | 1.060706 | -0.026613 | -0.043973 | -0.350735 | 0.005639 | -0.436489 | -0.055617 | 0.079832 | -0.02527 | 0.024521 | 0.655386 | -0.02527 | 1.361607 | -0.516479 | 2.107090 | -0.445230 | -0.071630 | 0.119816 | -1.130311 | 0.629518 | 1.354116 | -0.797900 | 0.751330 | -0.127267 | 0.338364 | 0.028560 | -0.166292 | -0.225270 | 0.036409 | -0.360561 | 0.109320 | 1.497027 | -0.366111 | -0.509092 | -0.095639 | -0.068016 | -0.158656 | -0.087905 | -0.015668 | 0.337441 | -0.755701 | 0.021108 | -0.813834 | 2.363797 | -0.113668 | 0.115301 | -0.356416 | 0.028161 | -0.804895 | -0.710017 | -0.234886 | -0.340277 | -0.909772 | -0.130764 | -0.008698 | -0.950407 | 0.048404 | 0.347110 | 0.516441 | 0.046677 | 0.286272 | 0.312193 | 0.715219 | -0.132603 | 0.127143 | -0.075038 | -0.246020 | -0.184645 | -0.107220 | -0.280314 | -0.115752 | -0.177845 | -0.817010 | -0.606301 | -0.759358 | -0.713858 | 1.647602 | -1.420067 | -0.806125 | -1.352105 | -0.836574 | 0.790157 | 0.011643 | 0.004157 | -0.956626 | 0.674711 | 1.061850 | -0.599599 | 0.170605 | -0.280398 | 0.012735 | -0.113536 | -0.371878 | -0.866787 | 0.275875 | 1.410823 | -0.047360 | -0.796364 | -0.067216 | 0.439115 | -0.322118 | 1.027345 | 0.021704 | -0.02527 | 0.087546 | 2.072138 | -0.333672 | -0.549396 | 0.432990 | -0.511979 | -0.691022 | -0.632172 | -1.410071 | 0.288995 | 1.198146 | -0.552939 | 0.669298 | -0.122128 | 0.574589 | -0.014720 | -0.367764 | -0.809806 | 1.458969 | 1.535095 | -0.836511 | -0.409237 | -0.044627 | -0.112621 | -0.205425 | -0.085908 | -0.010296 | 0.308905 | -0.733787 | 0.048082 | -0.702659 | 1.787174 | -0.635529 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | 0.409315 | -6.626947 | -0.943318 | 2.018285 | 25.273849 | 30.662679 | 1.644833 | 24.582485 | 31.051487 | 3.239803 | 26.607528 | 30.846836 | 1.984363 | 2.002900 | 3.852192 | 1.602703 | 0.751806 | -0.211916 | 0.450099 | 0.294966 | 1.079847 | 0.296140 | 0.385749 | 0.492947 | 0.002548 | -0.278290 | -0.221613 | -0.691776 | -0.232808 | -0.770689 | -0.224950 | -0.645708 | -0.322218 | -0.292200 | -0.362121 | -0.283360 | -0.101845 | -0.178804 | -0.308135 | -0.275049 |
| 4 | 0.242296 | 0.087328 | 1.117227 | -0.156616 | -0.047033 | -0.115954 | 0.187531 | 0.545066 | -0.149545 | -0.790478 | -0.599235 | 0.610329 | 0.558822 | 0.436541 | 0.179609 | 0.170486 | 0.560597 | -0.035232 | -0.971470 | 0.226789 | -0.217071 | -0.131402 | 0.143217 | 0.477615 | 0.278161 | 0.511055 | -1.927707 | 1.933338 | 2.595907 | -0.747983 | 0.488609 | -0.143239 | -0.283042 | -0.157178 | 0.283034 | -0.220697 | -1.544931 | 1.526523 | 0.348307 | -0.486722 | -0.352717 | 2.160530 | 5.044562 | 4.610846 | -0.759292 | -0.416700 | 4.108142 | 1.249991 | 0.565437 | 0.932419 | 0.343601 | 0.139051 | -1.035010 | 0.566594 | -1.603330 | -0.012134 | -2.291208 | 3.196132 | 1.125286 | 2.367580 | 2.396185 | 3.357289 | -0.051267 | -0.183091 | 2.801278 | -1.218466 | -0.02527 | -2.103044 | 1.447684 | -0.308969 | -0.658571 | -0.000635 | 0.011878 | 0.522895 | -0.303990 | 0.096184 | 0.217266 | 1.370004 | 0.605720 | 0.384333 | -0.113573 | 0.479856 | 0.484295 | -2.542422 | 0.146352 | -0.408413 | -1.532954 | 0.921198 | -1.530722 | -0.291280 | 1.187298 | -1.329232 | 1.090133 | 1.727662 | 0.252518 | 2.596610 | -0.180534 | -0.895297 | -2.229684 | 0.079873 | -0.249335 | -0.073477 | 1.005673 | 0.386589 | -0.037036 | -0.885992 | -0.891723 | 0.355990 | -0.467148 | -0.832490 | -0.923061 | -0.743690 | -0.853563 | 1.093794 | -0.857250 | 0.405580 | -0.013016 | 0.400423 | 1.422844 | 0.999190 | -1.674474 | -0.417387 | 0.018588 | 0.093308 | 0.008238 | -0.715202 | -0.456913 | -0.050491 | -0.848040 | -0.053135 | -0.923416 | -0.698264 | 0.334323 | 0.243235 | 0.307904 | 0.677096 | -0.049593 | -0.032225 | 0.072330 | 1.186248 | -0.067923 | -0.054183 | -0.186682 | -0.473984 | -0.547278 | -0.511640 | 0.055970 | -0.362736 | -0.341418 | 0.993435 | -0.689280 | -0.491453 | 0.498318 | -0.688398 | -0.937657 | -0.208081 | 0.071150 | -0.208074 | 0.434094 | -1.521526 | 1.865017 | -1.140057 | -1.180578 | 0.016784 | 1.617958 | -0.235251 | -0.471223 | 0.883704 | 1.333731 | -0.093808 | -0.201512 | -0.411253 | -0.070538 | -0.049404 | 0.250497 | -0.454548 | -0.117704 | 0.215739 | -0.056222 | 0.003899 | -0.02527 | -0.034577 | 0.679992 | -0.02527 | 0.066469 | -0.245902 | 0.817229 | 0.905201 | -0.061557 | 0.781710 | 0.348198 | 0.653548 | 0.309845 | -0.153759 | 0.836236 | -0.150596 | -0.973833 | 1.972255 | 0.863000 | 0.332995 | 0.871789 | 1.125291 | -1.024046 | -0.281132 | -0.023796 | -0.067565 | 0.724924 | -0.098365 | -0.032139 | -0.695773 | 0.816805 | 0.010407 | -0.736958 | 0.049852 | -0.140280 | 0.580287 | 0.555771 | 0.666680 | -0.705437 | -0.554910 | -0.050595 | -0.937810 | 0.538847 | -0.757365 | -0.993079 | 0.217941 | 0.261146 | 0.276529 | 1.162745 | -0.062011 | -0.032235 | 0.026246 | 1.095318 | 0.016031 | -0.071503 | -0.173164 | -0.440915 | -0.546668 | -0.531148 | 0.182493 | -0.322420 | -0.323104 | 1.326377 | -0.676612 | -0.575427 | 0.339739 | -0.861651 | -0.694167 | 0.236017 | 0.029343 | 0.235984 | 0.056396 | -1.360234 | 1.683432 | -1.062180 | -1.327268 | -0.226787 | 1.442789 | -0.326471 | -0.539177 | 0.769148 | 1.479864 | -0.016358 | -0.172177 | -0.437904 | 0.052665 | -0.046530 | 0.005874 | -0.415683 | -0.078703 | 0.198921 | -0.055474 | 0.099232 | -0.02527 | 0.033384 | 0.995632 | -0.02527 | -0.006240 | -0.297836 | 0.686675 | 1.011611 | 0.173528 | 0.919729 | 0.435291 | 0.534655 | 0.657919 | 0.003837 | 1.227916 | -0.142270 | -1.108754 | 1.835437 | 1.187769 | 0.415380 | 1.212762 | 0.530546 | -0.223680 | 1.684350 | -0.795204 | -0.232199 | 0.039992 | -0.068016 | 1.284098 | -0.096371 | -0.028660 | -0.664895 | 1.071951 | 0.041943 | -0.720353 | 0.149829 | -0.169488 | 0.016595 | 0.080387 | -0.004753 | -0.784307 | -0.465444 | -0.024438 | -0.761740 | -0.079994 | -0.980586 | -1.114137 | 1.713158 | 0.223769 | 0.416226 | 0.232819 | -0.053951 | -0.107767 | 0.060975 | 1.052813 | -0.071977 | -0.046079 | -0.156775 | -0.319343 | -0.440232 | -0.910799 | -0.097479 | -0.214219 | -0.172451 | 1.360716 | -0.861070 | -0.865110 | 0.603490 | -0.732689 | -0.949346 | -0.183654 | 0.088483 | -0.237263 | 0.439680 | -1.505619 | 1.808222 | -1.125250 | -1.223283 | -0.565664 | 1.214481 | -0.102277 | -0.525081 | 0.733556 | 0.127330 | -0.099798 | -0.777246 | -0.388966 | -0.012880 | -0.216080 | -1.007588 | -0.995922 | -0.746529 | 0.105504 | 1.513749 | -0.005524 | -0.02527 | -0.084140 | 1.812996 | -0.525826 | 0.371939 | 1.170660 | 0.186073 | -1.058096 | 1.334413 | 1.582335 | -1.199061 | 0.208094 | -0.168459 | 0.761221 | -0.139003 | -0.928427 | 3.219844 | 0.781380 | 0.093284 | -0.745562 | 2.451447 | -0.836511 | -0.266711 | -0.025401 | -0.112621 | 0.563636 | -0.094615 | -0.061635 | -0.657819 | 0.808501 | -0.011237 | -0.781583 | 0.079291 | -0.045134 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | 1.242477 | -0.770061 | 1.186942 | 0.658373 | 0.600786 | 0.613597 | 0.650256 | 1.049070 | 0.606361 | 0.428173 | 0.612497 | 0.523328 | -0.205168 | -0.546970 | -0.510902 | -0.538198 | 0.183094 | -0.212815 | -0.434630 | -0.289437 | -0.398435 | -0.314028 | -0.405707 | -0.362419 | 0.085279 | -0.270290 | -0.227409 | -0.496123 | -0.222385 | -0.503607 | -0.230791 | -0.454486 | -5.906917 | 26.867221 | 27.071429 | 26.913337 | -0.101845 | -0.178804 | -0.308135 | -0.275049 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1562 | -1.565963 | -0.392832 | -0.708802 | 3.843544 | -0.047908 | -3.039882 | 0.333062 | -1.631679 | -0.242250 | -0.629070 | -0.518851 | 1.238535 | 0.989573 | 0.363740 | 0.138372 | -0.178146 | 1.332251 | 0.258502 | 0.128246 | 0.318608 | -0.309375 | -1.756499 | 0.225633 | 0.438168 | 0.081670 | 0.353806 | 0.475813 | -0.352709 | -0.269264 | -0.383040 | -0.739085 | -0.181420 | -0.282873 | -0.077042 | 0.282865 | -0.065736 | -1.051282 | 1.016690 | 0.549945 | -0.820658 | 2.305151 | 1.622016 | -1.031348 | 0.488576 | -0.550876 | -0.425547 | 0.503348 | -1.660004 | 1.185173 | 1.117337 | 0.304703 | -1.243839 | 0.092281 | -1.167055 | -0.013992 | 2.206870 | 1.370599 | -0.584258 | -0.632907 | -0.676349 | -0.656011 | 0.903631 | -0.051050 | -0.053725 | 0.841426 | -0.112438 | -0.02527 | 0.026853 | 0.014663 | -0.090453 | 0.023691 | -0.122920 | 0.198176 | 0.090581 | 0.042515 | 0.619325 | -0.438757 | -0.687877 | 0.286498 | 1.151089 | -0.162119 | 0.960401 | 1.882216 | -0.095034 | 0.545424 | 0.167111 | -0.573633 | -0.252300 | 0.213708 | 1.387629 | 0.059725 | 0.032738 | -1.590248 | 0.127210 | -1.394746 | 0.092872 | -0.549500 | -0.407208 | -1.327459 | 0.079873 | 0.542156 | -0.073477 | -0.402743 | 0.239119 | -0.042342 | 0.072082 | -0.802294 | 0.954711 | -1.071374 | 1.163666 | -0.923061 | -1.095233 | 1.273905 | -1.174044 | 1.131241 | -0.217212 | 0.453468 | -2.045061 | -0.137296 | -0.564086 | -0.963925 | 0.870708 | 1.637766 | -0.782324 | 0.620281 | -2.049935 | 4.651071 | -0.050614 | 0.834941 | -1.774744 | 0.114347 | -1.184766 | 0.045654 | 0.269068 | 0.221523 | 0.399758 | -0.233262 | -0.030163 | 0.383028 | 0.862409 | -0.246721 | -0.507757 | 0.404880 | -0.385176 | 0.719396 | -0.645723 | -0.140954 | 0.128380 | 0.019809 | 2.781218 | 1.525341 | 0.264954 | 0.933257 | 0.216491 | -0.519525 | -0.258645 | -1.266742 | -0.258635 | -1.039477 | 0.361739 | -0.833645 | 1.274184 | 1.053443 | 0.576689 | -1.838111 | 0.052597 | -0.126019 | -0.202947 | -0.623072 | -0.504394 | -0.064406 | 0.057943 | -0.350093 | -0.218815 | -0.563943 | -0.032313 | -0.078907 | -0.812629 | -0.056709 | -0.218409 | -0.02527 | -0.438965 | -0.276144 | -0.02527 | -0.216214 | -0.141100 | -0.308839 | -0.081349 | -0.138327 | 0.046454 | 0.043014 | -0.213007 | 1.166223 | 0.395218 | 0.857697 | -0.109465 | 0.213750 | -0.399874 | -1.202834 | -0.644129 | 0.079980 | 0.123130 | -1.232373 | -0.281132 | -0.159921 | -0.067565 | -1.007089 | -0.082348 | -0.017622 | -0.106942 | -0.201901 | -1.933136 | 0.507824 | -0.722530 | -0.336620 | 0.755401 | -0.828645 | 0.653567 | -2.260479 | 5.205323 | -0.050684 | 0.683847 | -1.821385 | 0.525306 | -1.363625 | 0.127694 | 0.272853 | 0.171250 | 0.859965 | -0.241554 | -0.030025 | 0.469572 | 0.685393 | -0.220960 | -0.557003 | 0.254837 | -0.366265 | 0.539819 | -0.644302 | -0.183753 | 0.120572 | 0.026762 | 2.458833 | 1.634786 | 0.466181 | 1.270215 | 0.146181 | -0.304156 | -0.306786 | -0.685543 | -0.306770 | -1.120152 | 0.140923 | -0.773818 | 1.571747 | 0.441995 | 0.915764 | -1.817393 | -0.037749 | -0.091702 | -0.294711 | -0.670080 | -0.817012 | -0.032910 | -0.146137 | -0.392520 | -0.194149 | -0.236147 | 0.046920 | 0.010381 | -0.706505 | -0.055801 | -0.226745 | -0.02527 | -0.520048 | -0.243398 | -0.02527 | -0.176159 | -0.117217 | -0.304802 | -0.092305 | -0.102274 | 0.113313 | 0.173072 | -0.228772 | 1.639112 | 0.404706 | 0.919537 | -0.092261 | 0.643181 | -0.459531 | -1.063925 | -0.715179 | 0.372510 | -0.620467 | -0.461538 | -0.001554 | -0.795204 | -0.232199 | -0.140849 | -0.068016 | -0.891431 | -0.070973 | -0.013173 | -0.111366 | -0.281865 | -2.135353 | 0.756636 | -0.835665 | -0.385623 | 1.651159 | -0.713857 | 0.642822 | -2.057114 | 1.207412 | -0.095886 | 1.277674 | -1.821897 | 0.313649 | 1.351390 | 2.077881 | 0.247503 | 0.291227 | -0.269137 | -0.267221 | -0.085780 | 0.371125 | 0.738522 | -0.224183 | -0.450970 | 0.144600 | -0.262477 | 0.117981 | -0.207216 | -0.150598 | -0.035743 | -0.111064 | 2.656629 | 1.300215 | 0.195608 | 0.866567 | 0.297756 | -0.500939 | -0.233842 | -1.209912 | -0.288268 | -1.035502 | 0.379675 | -0.848327 | 1.016025 | 0.947009 | 0.772191 | -1.844566 | 0.209382 | -0.128751 | -0.219507 | 1.762115 | -0.495667 | 0.209869 | -0.013695 | -0.391292 | -0.247846 | -0.083761 | 0.233747 | 0.100760 | -0.784323 | -0.535573 | -0.178917 | -0.02527 | -0.477156 | -0.294032 | -0.087950 | -0.299575 | -0.356009 | -0.399653 | -0.181576 | -0.477638 | -0.016096 | -0.229221 | 0.816552 | 0.440574 | 0.890838 | -0.102969 | 0.120889 | -0.518390 | -1.245561 | -0.809806 | -0.745562 | -0.068004 | -0.120905 | -0.266711 | -0.153897 | -0.112621 | -1.015741 | -0.079661 | 0.015976 | -0.107903 | -0.192833 | -1.975969 | 0.565560 | -0.710122 | -0.531515 | 2.484190 | 1.259571 | -0.031556 | 0.912071 | 0.774835 | -0.367957 | 0.555543 | 0.763727 | -0.000351 | -0.335745 | 0.834203 | 0.426927 | -0.339162 | 0.869951 | 0.012182 | -0.334262 | 1.219519 | 0.147139 | 0.397025 | 0.005147 | 0.183094 | -0.967761 | -0.508358 | -0.717820 | -0.461573 | -0.661508 | -0.530834 | -0.524199 | 0.332578 | -0.314650 | -0.237261 | -0.440165 | -0.242357 | -0.387035 | -0.240947 | -0.393931 | -0.381004 | -0.059222 | 0.014418 | -0.056136 | -1.186838 | -0.303775 | -0.203434 | 1.103214 |
| 1563 | 0.515516 | 0.332706 | -0.067591 | -0.615706 | -0.058749 | -0.426519 | -0.148310 | -0.400403 | -0.348198 | -1.016450 | -0.205352 | 1.247938 | 0.056105 | -0.422496 | 0.133707 | -0.153243 | 1.347336 | -0.364415 | -0.606891 | -1.262278 | -1.428886 | 0.094875 | 0.219771 | -0.659024 | -0.719613 | -1.196796 | 0.967442 | -0.896913 | -1.293191 | 1.936736 | -0.629568 | 0.000404 | -0.317987 | -0.170197 | 0.318063 | -0.238464 | -0.966394 | 0.872510 | 0.475481 | -0.172430 | -0.663186 | -1.634220 | 1.826570 | 0.964694 | -1.695845 | -1.563191 | 0.362015 | 0.013721 | -0.145436 | -0.597353 | -1.056718 | -0.493547 | 1.291527 | -0.738645 | -0.663254 | -0.916241 | -0.958913 | 1.302511 | -0.103847 | 0.567128 | 0.601764 | 0.632520 | -0.052686 | -0.629833 | 0.318389 | 0.852146 | -0.02527 | 0.026853 | 0.014663 | -0.090453 | 0.023691 | -0.122920 | 0.198176 | 0.090581 | 0.042515 | 0.000240 | 0.793771 | 2.307840 | 0.675793 | 1.208858 | 0.119449 | -0.519932 | -4.108387 | -1.860871 | 1.044264 | 0.167111 | 0.385687 | -0.452675 | 1.048848 | -0.618756 | -1.067849 | 0.940718 | 1.343209 | -0.591361 | 0.605504 | 1.876785 | -1.052635 | 0.421167 | -0.210363 | 0.079873 | 0.880189 | -0.073477 | 0.171026 | 0.291787 | -0.235058 | 0.072082 | 1.098059 | -0.166283 | 1.244827 | -2.471357 | 0.744088 | 0.662479 | 0.444371 | 0.128083 | -2.462711 | 2.327121 | 0.336868 | 1.533222 | 0.977090 | -0.581057 | 1.797707 | 1.190417 | -0.521138 | 1.805532 | 1.863553 | 0.131209 | -0.232897 | -0.051012 | 0.062750 | -0.209645 | -0.146725 | -0.634151 | 0.096056 | 0.143593 | 0.230613 | 0.250897 | -0.228935 | -0.042459 | 0.372314 | 0.959150 | -0.202021 | 0.289471 | -0.385227 | -0.803096 | -0.885372 | -0.675961 | -0.698906 | -0.350355 | 0.039468 | -0.079235 | -0.847468 | -1.310894 | -1.615692 | -0.399901 | -0.394415 | 2.120649 | 0.778770 | 2.120530 | -0.814958 | -1.823382 | 0.068267 | 0.455337 | 0.148241 | -0.850708 | 0.539464 | -0.646591 | 0.408491 | -0.128567 | -0.639018 | -0.096343 | -0.255717 | -0.081676 | 0.071104 | -0.184080 | 0.792687 | -0.621478 | -0.323724 | 0.113631 | -0.056673 | 0.042976 | -0.02527 | 0.234633 | 2.805338 | -0.02527 | -0.216214 | -0.141100 | -0.308839 | -0.081349 | -0.138327 | 0.046454 | 0.043014 | -0.213007 | -0.373154 | 3.262096 | 0.823359 | -0.132713 | 0.877492 | 0.728421 | -1.483505 | 2.398914 | 0.415335 | -0.820079 | -0.746275 | -0.281132 | -0.411855 | -0.067565 | -1.131247 | -0.082348 | -0.030946 | -0.106942 | -1.045347 | -0.209884 | 1.400820 | 0.683105 | 0.666402 | -0.809775 | 2.615704 | 2.106780 | 0.412988 | -0.298734 | -0.051003 | -0.030656 | -0.346240 | -0.278830 | -0.668851 | -0.179147 | 0.155787 | 0.181781 | 0.421390 | -0.201139 | -0.040246 | 0.401368 | 0.789608 | -0.163564 | 0.132094 | -0.353921 | -0.738136 | -0.874097 | -0.666651 | -0.606104 | -0.314004 | 0.074720 | -0.076530 | -0.882870 | -1.134170 | -1.677064 | -0.213012 | -0.331733 | 2.115790 | 0.848483 | 2.115589 | -0.832347 | -1.697300 | -0.072722 | 0.428804 | 0.280273 | -0.713383 | 0.424743 | -0.772107 | 0.255993 | -0.050993 | -0.776211 | -0.079526 | -0.183223 | -0.147993 | -0.016316 | -0.194877 | -0.146840 | -0.452375 | -0.214832 | 0.092456 | -0.055814 | 0.063546 | -0.02527 | 0.010790 | 3.212124 | -0.02527 | -0.176159 | -0.117217 | -0.304802 | -0.092305 | -0.102274 | 0.113313 | 0.173072 | -0.228772 | -0.237154 | 2.809917 | 0.807399 | -0.127267 | 0.746965 | 0.685606 | -1.439750 | 2.337332 | 0.288485 | 1.384524 | 0.156892 | -0.563522 | -0.795204 | -0.232199 | -0.444404 | -0.068016 | -1.155816 | -0.079439 | -0.029137 | -0.111366 | -1.094155 | -0.153388 | 1.289474 | 0.320840 | 1.062422 | -0.542185 | 1.764823 | 1.787376 | 0.140692 | 0.038109 | -0.271848 | 0.086339 | -0.160598 | -0.109978 | 1.544188 | 0.932886 | 0.130112 | 0.303156 | 0.171348 | -0.253418 | -0.206302 | 0.356342 | 0.824064 | -0.184244 | 0.253812 | -0.292786 | -0.501177 | -0.719757 | -0.430863 | -0.283724 | -0.203237 | -0.066757 | -0.192567 | -0.577941 | -0.700007 | -1.591138 | -0.341587 | -0.413504 | 2.121508 | 0.771139 | 2.118186 | -0.806585 | -1.813470 | 0.051164 | 0.536279 | 0.218184 | -1.010864 | 0.463342 | -0.459683 | 0.548316 | -0.140978 | -0.799540 | -0.087119 | -0.367750 | -0.051410 | 0.099229 | -0.293018 | 0.983730 | -0.866355 | -0.819376 | 0.086966 | 0.575969 | 0.033020 | -0.02527 | 0.247001 | 1.126094 | -0.087950 | -0.299575 | -0.356009 | -0.399653 | -0.181576 | -0.477638 | -0.016096 | -0.229221 | -0.366019 | 3.142844 | 0.698364 | -0.123307 | 0.716965 | 1.102127 | -1.451511 | 1.705206 | 1.963503 | -0.698812 | 1.287944 | -0.266711 | -0.383910 | -0.112621 | -1.181297 | -0.080192 | -0.050326 | -0.107903 | -1.035628 | -0.199416 | 1.488949 | 0.643881 | 0.335743 | 0.290199 | 1.129045 | -0.708937 | 1.154158 | -0.532946 | -0.575495 | -0.685681 | -0.659041 | -0.522218 | 0.090654 | -0.664556 | -0.458990 | 0.011431 | -0.938291 | -0.484619 | 0.121788 | -0.332841 | -0.075452 | -0.054999 | -0.009389 | 0.551118 | -0.443168 | -0.680389 | -0.138124 | -0.826467 | -0.285764 | -0.721291 | 0.054580 | 0.423157 | 0.289297 | -0.266237 | 0.174975 | -0.269572 | 0.244518 | -0.268684 | 0.095786 | -0.763115 | -0.129116 | -0.066269 | -0.124122 | -1.186838 | -0.303775 | -0.203434 | 1.103214 |
| 1564 | -0.485064 | -1.447412 | 0.195701 | -0.647916 | -0.059689 | -0.274555 | -0.114726 | -0.017068 | -0.030354 | 0.027324 | 0.116185 | -0.129271 | -0.013712 | -0.050309 | -0.023220 | 0.095779 | -0.137683 | -0.118103 | 1.227962 | 0.742573 | 0.025970 | -0.214442 | -0.319899 | 0.501847 | 0.026209 | 0.476227 | 0.511181 | -0.325524 | -0.554870 | -0.339666 | 0.259947 | -0.586003 | 0.296800 | -0.058038 | -0.296808 | -1.078084 | -1.088462 | -0.436401 | 0.584249 | -0.207446 | -0.838281 | -0.422850 | -0.239914 | -0.729072 | 0.821503 | 0.643511 | -0.485771 | 0.972791 | 0.055066 | -0.143465 | -2.029161 | 0.948189 | 0.571980 | 0.218217 | -0.187705 | -1.100630 | -0.128325 | -0.055044 | -0.465978 | -0.343651 | -0.173715 | -0.668814 | -0.051325 | 1.271895 | -0.180862 | -0.224529 | -0.02527 | 0.026853 | 0.014663 | -0.090453 | 0.023691 | -0.122920 | 0.198176 | 0.090581 | 0.042515 | 0.630180 | -0.100806 | 1.530776 | 0.348785 | 0.234451 | -0.597095 | 0.423939 | -0.282676 | 0.896312 | 0.146352 | 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0.156721 | -0.711652 | -0.463181 | 0.204353 | 0.061953 | 0.079403 | -0.039846 | -0.056812 | -0.322616 | -0.02527 | -0.057489 | -0.491396 | -0.02527 | -0.216214 | -0.141100 | -0.308839 | -0.081349 | -0.138327 | 0.046454 | 0.043014 | -0.213007 | -0.543967 | -0.824730 | -0.627422 | -0.121984 | -0.408740 | -0.208735 | 0.118378 | -0.355645 | -0.385789 | 1.773747 | 1.892543 | -0.281132 | -0.153826 | -0.067565 | 0.140820 | -0.090357 | -0.015042 | -0.106942 | -1.001531 | -0.504990 | 0.000440 | -0.207518 | -0.064390 | -0.696025 | -0.031512 | 0.345802 | 1.611649 | -0.259643 | -0.051024 | -0.058622 | 0.538847 | -0.190030 | -0.174790 | -0.215246 | -0.054932 | -0.024886 | -0.253888 | -0.092114 | -0.030926 | -0.110162 | -0.018291 | -0.198001 | -0.024519 | 0.296821 | -0.056548 | -0.017742 | -0.553953 | -0.289730 | -0.216071 | 0.034070 | 0.569296 | 1.346431 | 0.107481 | 0.293447 | -0.568717 | 0.889513 | 0.669530 | -1.948509 | 0.669459 | 0.585958 | -0.512358 | 1.245033 | 0.923703 | 0.341091 | -0.531105 | -0.674444 | 0.307463 | -0.531709 | -0.417702 | 1.267920 | -0.199545 | -0.044525 | 0.036172 | -0.566420 | -0.406425 | -0.235510 | 0.008340 | 0.050997 | -0.050603 | -0.055883 | -0.260983 | -0.02527 | -0.168694 | -0.830903 | -0.02527 | -0.176159 | -0.117217 | -0.304802 | -0.092305 | -0.102274 | 0.113313 | 0.173072 | -0.228772 | -0.418186 | -0.797900 | -0.566289 | -0.112265 | -0.634602 | -0.262417 | -0.317759 | -0.451382 | -0.299692 | 0.010734 | -0.413967 | 1.684350 | 1.779351 | -0.232199 | -0.127932 | -0.068016 | -0.076375 | -0.079439 | -0.018566 | -0.111366 | -0.992619 | -0.265378 | -0.028599 | -0.285743 | 0.021073 | -0.568105 | 0.198218 | 0.331666 | 1.295984 | -0.208957 | -0.266884 | 0.255949 | 0.150290 | -0.196086 | -0.062828 | -0.112462 | -0.057594 | -0.033462 | -0.237741 | -0.102436 | -0.087309 | -0.098927 | -0.038452 | -0.177581 | -0.139064 | 0.123441 | -0.064037 | -0.079644 | -0.420834 | -0.184824 | -0.161833 | -0.108095 | 0.294278 | 0.891558 | 0.531757 | 0.588126 | -0.237293 | 1.173831 | 0.852867 | -1.729786 | 0.977653 | 1.052020 | -0.524201 | 1.430893 | 0.416207 | -0.128357 | -0.458755 | -0.793889 | 0.020655 | -0.534056 | -0.377310 | 0.642244 | -0.208471 | 2.062764 | 0.183179 | -0.739642 | -0.615792 | 0.914804 | 0.172745 | 0.246566 | -0.022694 | -0.886657 | -0.272201 | -0.02527 | -0.055272 | -0.401450 | -0.087950 | -0.299575 | -0.356009 | -0.399653 | -0.181576 | -0.477638 | -0.016096 | -0.229221 | -0.612132 | -0.827029 | -0.674940 | -0.113902 | -0.398108 | -0.070210 | 0.065567 | 0.597223 | -0.745562 | -0.024413 | -0.836511 | -0.266711 | -0.147723 | -0.112621 | 0.272020 | -0.086728 | 0.025348 | -0.107903 | -0.998576 | -0.493416 | 0.016377 | -0.214444 | -0.052475 | 0.253632 | 0.280626 | -0.370247 | 0.238229 | -0.330933 | -2.688488 | -0.399489 | 0.057756 | -0.183115 | 0.227424 | 0.075961 | -0.327178 | 0.111470 | 0.527146 | -0.070561 | 0.243105 | -0.276760 | -0.989172 | -0.761164 | -1.012500 | -0.901139 | 1.315972 | 1.994281 | 1.314476 | 1.972381 | 1.277064 | 2.112019 | 0.924364 | -0.008730 | 0.878700 | -0.254531 | 0.280853 | -0.259603 | 0.449625 | -0.256565 | 0.086877 | -0.410397 | -0.000978 | 0.068209 | -0.002413 | -0.142330 | -0.894549 | -0.971243 | -0.598172 |
| 1565 | -1.627087 | 0.450658 | -0.800728 | -0.481365 | -0.046334 | -0.373534 | -0.058753 | -0.008941 | -0.421038 | 0.328619 | 0.405569 | -0.833225 | 0.261402 | -0.649397 | -0.018534 | 0.211989 | -0.959418 | -0.402628 | -1.091005 | 0.553346 | -0.077344 | -0.141555 | 0.238995 | 0.470853 | 0.261259 | 0.551834 | 0.292666 | 1.470691 | 1.654016 | -0.733961 | -0.383723 | 0.173297 | 0.061073 | -0.209760 | -0.061080 | 0.029017 | -1.172678 | 0.138103 | 0.546598 | -0.547360 | 2.414863 | 1.619158 | -1.189425 | 0.419429 | -0.488088 | 0.429991 | 0.886725 | -1.441935 | -0.163664 | -0.227518 | 1.043760 | -1.199704 | 0.188221 | -0.498547 | 0.159531 | 2.114068 | 1.382631 | -0.536638 | -0.875098 | -1.008684 | -0.943958 | 1.007494 | -0.051031 | 1.170680 | 1.370095 | -0.256285 | -0.02527 | 0.026853 | 0.014663 | -0.090453 | 0.023691 | -0.122920 | 0.198176 | 0.090581 | 0.042515 | 0.579009 | 0.754012 | -1.121961 | -0.219585 | -3.369638 | 0.026240 | -0.005218 | 1.982803 | -1.272259 | -0.951096 | 0.167111 | 0.385687 | -0.500601 | -0.197643 | 2.293377 | 1.187298 | 0.032738 | -1.871974 | 0.355846 | -2.218378 | 1.282148 | 0.993449 | 1.587538 | -0.128343 | 0.079873 | 0.253592 | -0.073477 | -2.897083 | 0.260186 | -0.055114 | 0.072082 | 1.086880 | -0.557383 | -0.769261 | 0.869624 | -0.645203 | -0.655805 | 0.716115 | -0.366646 | 1.319422 | 0.716976 | -0.362817 | -1.094580 | -0.226447 | -1.095863 | -0.699586 | -0.241637 | -0.190338 | -0.654047 | -1.075788 | 0.489314 | -0.194428 | -0.050889 | 0.034464 | -0.209645 | 0.111084 | -1.113111 | 1.493581 | -0.159024 | -0.254055 | 0.444848 | 0.390348 | -0.035735 | -0.216941 | -0.824919 | -0.237781 | -0.457220 | 0.873242 | -0.527966 | -0.321803 | -0.565090 | -0.346084 | 0.268699 | -0.004764 | -0.526181 | 0.259843 | 0.874283 | 1.464289 | -1.811247 | 1.763739 | -0.455281 | 0.280196 | -0.455260 | -0.921626 | 0.754420 | -0.437511 | 2.301521 | 1.931891 | 0.953964 | -1.871045 | -0.546707 | -0.297229 | -0.311032 | -1.051588 | 0.598105 | -0.086725 | 0.054143 | -0.182360 | -0.149954 | -0.935401 | -0.101049 | -0.073209 | -0.795640 | -0.056754 | 0.232285 | -0.02527 | -0.623403 | -0.528533 | -0.02527 | -0.216214 | -0.141100 | -0.308839 | -0.081349 | -0.138327 | 0.046454 | 0.043014 | -0.213007 | -0.438384 | 0.273223 | 0.411303 | -0.014685 | -0.611892 | -0.270070 | -0.167455 | -0.048549 | -0.301951 | 1.184241 | 3.003625 | -0.281132 | -0.005510 | -0.067565 | -0.187270 | -0.082348 | -0.012039 | -0.106942 | -1.111070 | 0.041996 | 0.673570 | 0.324016 | 0.286689 | -0.115245 | -0.742290 | -0.922280 | 0.624397 | -0.199619 | -0.050903 | -0.062653 | -0.051211 | 0.031971 | -1.425382 | 1.842392 | -0.171998 | -0.246726 | 0.629760 | 0.337356 | -0.033671 | -0.212468 | -0.823590 | -0.240586 | -0.435628 | 0.654953 | -0.434725 | -0.360602 | -0.582822 | -0.302198 | 0.160357 | 0.000727 | -0.306277 | 0.179330 | 0.652428 | 1.828809 | -1.740452 | 2.177729 | 0.028367 | -0.006401 | 0.024710 | -0.848464 | 0.794205 | -0.399388 | 1.576953 | 1.260279 | 0.778253 | -1.960847 | 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-0.459688 | 0.302095 | -0.454035 | -0.920192 | 0.776640 | -0.441777 | 2.002079 | 1.796267 | 1.207808 | -1.874744 | -0.558345 | -0.347986 | -0.337376 | 0.006545 | 0.605277 | -0.260688 | -0.014458 | -0.223944 | -0.150932 | -0.331707 | 0.344847 | 0.279432 | -0.769930 | -0.709347 | 0.141370 | -0.02527 | -0.676760 | -0.413868 | -0.087950 | -0.299575 | -0.356009 | -0.399653 | -0.181576 | -0.477638 | -0.016096 | -0.229221 | -0.516152 | 0.214463 | 0.461007 | -0.020889 | -0.408909 | -0.312258 | -0.181428 | 0.765505 | 0.597014 | 0.604058 | 0.548665 | -0.266711 | -0.012314 | -0.112621 | 0.237820 | -0.081229 | 0.046233 | -0.107903 | -1.103051 | 0.082349 | 0.777677 | 0.337131 | 0.349663 | 2.484190 | 1.259571 | -0.031556 | 0.912071 | -0.532946 | -0.575495 | -0.685681 | -0.659041 | -0.522218 | 0.090654 | -0.664556 | -0.458990 | 0.011431 | -0.938291 | -0.484619 | 0.121788 | 0.188590 | -0.664228 | -0.561343 | -0.698785 | 0.183094 | -0.967761 | -0.508358 | -0.717820 | -0.461573 | -0.661508 | -0.530834 | -0.524199 | 0.221215 | -0.698253 | -0.223931 | 0.085603 | -0.219605 | -0.010759 | -0.227906 | 0.205751 | 0.089286 | 0.144634 | -0.012478 | 0.139431 | 0.383973 | 0.911855 | 0.773779 | -0.065629 |
| 1566 | -0.946420 | -0.562403 | -0.173894 | 3.454187 | -0.045885 | -2.580044 | 0.187531 | -0.017068 | -0.030354 | 0.027324 | 0.116185 | -0.129271 | -0.013712 | -0.050309 | -0.023220 | 0.095779 | -0.137683 | -0.009910 | -0.015195 | 0.477096 | -0.106136 | -0.105474 | -0.043685 | 0.552565 | 0.369541 | 0.563095 | 1.031730 | 0.545398 | 0.545016 | -0.536346 | -0.985028 | -0.135127 | -0.328732 | -0.129391 | 0.328724 | 0.012567 | -0.969082 | 1.031196 | 0.518152 | -0.259544 | 1.167129 | 0.079649 | 0.725827 | 1.302197 | -1.610368 | -1.314262 | 0.812962 | -0.359360 | 0.018611 | -0.261140 | 0.693680 | -1.493936 | 0.763859 | -0.533855 | -0.113521 | 1.151923 | 0.001477 | 0.906761 | -0.764929 | -0.485730 | -0.537045 | 1.684051 | -0.051128 | -0.149337 | 1.480992 | 0.683742 | -0.02527 | 0.026853 | 0.014663 | -0.090453 | 0.023691 | -0.122920 | 0.198176 | 0.090581 | 0.042515 | 0.274118 | 0.614856 | -0.851329 | -0.515449 | -0.893574 | 0.002938 | -2.245567 | 1.979374 | -0.435810 | -0.119696 | 0.742635 | -0.573633 | -0.410227 | 0.110695 | -1.034090 | 0.623511 | 0.032738 | -1.731907 | -0.134088 | 0.958489 | 0.437136 | 0.121348 | 0.617090 | -0.783351 | 0.079873 | -0.183378 | -0.073477 | -1.304248 | 0.228585 | 0.190016 | 0.072082 | -0.802294 | 0.784913 | -0.064330 | -0.023597 | 0.003133 | -0.128491 | -0.145599 | -0.061893 | 0.039942 | 0.010639 | 0.336868 | 0.166449 | -0.025858 | -0.115280 | 0.001054 | -0.163269 | -0.103285 | 1.210622 | -1.047009 | -2.896338 | 0.807219 | -0.050865 | -0.197476 | -1.696489 | -0.203835 | -0.189133 | -0.229269 | -0.055691 | -0.029552 | -0.266101 | -0.098441 | -0.031140 | -0.088376 | -0.035340 | -0.246721 | -0.290446 | -0.121519 | -0.437416 | -0.840985 | -0.457579 | -0.731727 | -0.321465 | 0.137761 | 0.993435 | 0.101656 | 1.126419 | -0.154092 | -0.014180 | 0.260768 | -1.910386 | -2.175046 | -1.910286 | 0.274092 | -0.352833 | 0.248649 | 1.310443 | 0.763600 | 1.191864 | -1.142096 | -0.109942 | -0.107923 | -0.480712 | -0.923512 | -0.268687 | 0.232127 | 0.706648 | 0.529575 | -0.172501 | -0.174027 | 0.880892 | 0.521814 | -0.183854 | -0.056578 | 0.457198 | -0.02527 | 0.304513 | 0.834146 | -0.02527 | -0.216214 | -0.141100 | -0.308839 | -0.081349 | -0.138327 | 0.046454 | 0.043014 | -0.213007 | 0.385079 | 0.090231 | 0.218152 | 0.044329 | 1.858361 | -0.207308 | 0.409373 | -0.309115 | -0.227428 | 1.714797 | -1.301816 | -0.281132 | 0.232201 | -0.067565 | 0.222073 | -0.090357 | -0.036522 | -0.106942 | 0.126714 | -1.138428 | 0.000440 | -0.207518 | -0.064390 | 0.139884 | 0.593277 | -1.135841 | -2.698301 | 0.675064 | -0.050862 | -0.264877 | -2.116414 | -0.190030 | -0.174790 | -0.215246 | -0.054932 | -0.024886 | -0.253888 | -0.092114 | -0.030926 | -0.110162 | -0.018291 | -0.236883 | -0.325999 | -0.116756 | -0.401261 | -0.838868 | -0.471665 | -0.606104 | -0.284930 | 0.055993 | 1.771098 | -0.074058 | 1.114599 | -0.075349 | -0.162446 | 0.219797 | -1.789476 | -2.195740 | -1.789327 | 0.364923 | -0.463710 | 0.232622 | 2.302209 | 1.388827 | 1.131124 | -1.444385 | -0.200939 | -0.076482 | -0.570567 | -0.814023 | -0.717522 | 0.211882 | 0.814471 | 0.533788 | -0.163745 | 0.130354 | 0.860045 | 0.367318 | -0.241278 | -0.055727 | 0.214922 | -0.02527 | 0.188370 | 1.415977 | -0.02527 | -0.176159 | -0.117217 | -0.304802 | -0.092305 | -0.102274 | 0.113313 | 0.173072 | -0.228772 | 0.385595 | 0.204272 | 0.737313 | 0.022761 | 1.702823 | -0.271803 | 0.468991 | -0.300641 | -0.047616 | -0.137784 | -0.651824 | 1.684350 | -1.438842 | -0.232199 | 0.298337 | -0.068016 | 0.497193 | -0.087905 | -0.034816 | -0.111366 | 0.090434 | -1.367049 | -0.028599 | -0.285743 | 0.021073 | -0.091127 | 1.310477 | -1.015193 | -2.939479 | -0.508983 | -0.296168 | 0.089689 | -1.727827 | -0.196086 | -0.062828 | -0.112462 | -0.057594 | -0.033462 | -0.237741 | -0.102436 | -0.087309 | -0.098927 | -0.038452 | -0.223371 | -0.260214 | -0.112938 | -0.301321 | -0.686776 | 0.963659 | -0.317258 | -0.200223 | -0.095893 | 0.820695 | -0.131450 | 0.454699 | -0.097351 | 0.088625 | 0.307225 | -1.862164 | -2.070791 | -1.965773 | 0.272110 | -0.337871 | 0.226913 | 1.185629 | 0.749976 | 1.035785 | -1.194837 | 0.354985 | -0.076423 | -0.505441 | -0.908102 | -0.262201 | 1.886552 | 0.617439 | 0.544041 | -0.259991 | 1.055261 | 1.274722 | 1.559893 | -0.215913 | -0.002437 | 0.343763 | -0.02527 | 0.294063 | 0.034151 | -0.087950 | -0.299575 | -0.356009 | -0.399653 | -0.181576 | -0.477638 | -0.016096 | -0.229221 | 0.235343 | 0.044858 | 0.252202 | 0.031174 | 1.839280 | -0.256619 | 0.710201 | -0.809806 | -0.745562 | 0.534026 | 0.127528 | -0.266711 | 0.208559 | -0.112621 | 0.434436 | -0.086752 | -0.089693 | -0.107903 | 0.124663 | -1.181412 | 0.016377 | -0.214444 | -0.052475 | 0.436465 | 0.215364 | 0.645824 | 0.140162 | -0.532946 | -0.575495 | -0.685681 | -0.659041 | -0.522218 | 0.090654 | -0.664556 | -0.458990 | 0.011431 | -0.938291 | -0.484619 | 0.121788 | 1.046504 | -0.462007 | -0.089919 | -0.567577 | -0.722302 | -0.498057 | 0.900656 | -0.139469 | 0.585532 | -0.292414 | 0.966237 | 0.075943 | -0.098994 | -0.068126 | -0.250822 | 0.530452 | -0.256010 | 0.660634 | -0.252776 | 0.502828 | -0.410397 | 0.162107 | 0.041313 | 0.156574 | -0.790087 | -0.031111 | -0.273235 | 0.406375 |
1567 rows × 446 columns
print('''\n\033[1m''' + '''Fiting PCA''' + '''\033[0m''')
pca = PCA()
pca.fit(x)
Fiting PCA
PCA()
print('''\n\033[1m''' + '''Eigen Values''' + '''\033[0m''')
print(pca.explained_variance_)
Eigen Values
[2.55635884e+01 1.72100540e+01 1.33386388e+01 1.19466473e+01
9.78901151e+00 9.27933513e+00 8.61370084e+00 8.47404781e+00
7.61181070e+00 6.86557054e+00 6.27013184e+00 6.16312150e+00
5.99857396e+00 5.92839190e+00 5.62471343e+00 5.38044737e+00
5.33836225e+00 5.17333422e+00 4.97850609e+00 4.81947401e+00
4.73638018e+00 4.61723682e+00 4.49075659e+00 4.43154120e+00
4.36902753e+00 4.31258355e+00 4.08219537e+00 4.03970683e+00
3.94127690e+00 3.84716361e+00 3.84417868e+00 3.71282249e+00
3.66012781e+00 3.56130027e+00 3.55139071e+00 3.49565193e+00
3.43002042e+00 3.31466495e+00 3.28279911e+00 3.19915991e+00
3.18623901e+00 3.14285265e+00 3.09721328e+00 3.07818706e+00
2.97617082e+00 2.92092939e+00 2.87010710e+00 2.83910873e+00
2.80664545e+00 2.72530237e+00 2.69441167e+00 2.62744228e+00
2.60971211e+00 2.59877738e+00 2.53870765e+00 2.51235640e+00
2.46393202e+00 2.42723299e+00 2.39584692e+00 2.37641411e+00
2.29002450e+00 2.26916179e+00 2.24793196e+00 2.21367545e+00
2.19960102e+00 2.14155019e+00 2.11267800e+00 2.06650398e+00
2.05320539e+00 2.00465833e+00 1.98881807e+00 1.94277634e+00
1.93897560e+00 1.84989587e+00 1.79493122e+00 1.77586873e+00
1.70651200e+00 1.68997344e+00 1.67595042e+00 1.63687396e+00
1.58789849e+00 1.55254011e+00 1.54122067e+00 1.50648766e+00
1.49096218e+00 1.45753736e+00 1.44372324e+00 1.37890787e+00
1.37744754e+00 1.36096440e+00 1.33495178e+00 1.32242792e+00
1.28733326e+00 1.25787068e+00 1.24830743e+00 1.22631628e+00
1.19918139e+00 1.18692692e+00 1.16881060e+00 1.16315355e+00
1.14113793e+00 1.13859247e+00 1.11787095e+00 1.10825619e+00
1.07246774e+00 1.06210762e+00 1.05551651e+00 1.03479617e+00
1.01765509e+00 1.00346201e+00 9.99267181e-01 9.85559154e-01
9.73492971e-01 9.58048161e-01 9.40882943e-01 9.38543306e-01
9.31213026e-01 9.23504282e-01 9.13321018e-01 9.03614861e-01
8.97354121e-01 8.95316425e-01 8.75912924e-01 8.60193028e-01
8.60042003e-01 8.48067326e-01 8.39200541e-01 8.31680294e-01
8.26546493e-01 8.08471148e-01 8.02580909e-01 7.96883169e-01
7.81555827e-01 7.71360257e-01 7.63998001e-01 7.55128391e-01
7.49481619e-01 7.41975939e-01 7.24012147e-01 7.16733696e-01
7.06667776e-01 7.03139906e-01 6.95660851e-01 6.90643878e-01
6.77385628e-01 6.71965316e-01 6.59309762e-01 6.53764723e-01
6.45725448e-01 6.35658550e-01 6.21975952e-01 6.14540812e-01
6.12257757e-01 6.03494683e-01 5.97693739e-01 5.91334475e-01
5.81492224e-01 5.77819627e-01 5.69961640e-01 5.67423201e-01
5.60139751e-01 5.51308110e-01 5.38123339e-01 5.35393736e-01
5.21270481e-01 5.09238022e-01 5.07389200e-01 5.05762903e-01
4.97424942e-01 4.93928324e-01 4.75257950e-01 4.69445097e-01
4.54843565e-01 4.47673639e-01 4.39790528e-01 4.32877823e-01
4.31078173e-01 4.26159016e-01 4.20811579e-01 4.06346327e-01
3.96325045e-01 3.83425084e-01 3.77121429e-01 3.64521648e-01
3.61747286e-01 3.53565822e-01 3.42208378e-01 3.40986616e-01
3.28139180e-01 3.14447033e-01 3.12480678e-01 3.03235393e-01
2.98672530e-01 2.85394976e-01 2.82078961e-01 2.65450540e-01
2.64128744e-01 2.59614413e-01 2.50559615e-01 2.43702743e-01
2.39046231e-01 2.32206475e-01 2.26827171e-01 2.17499194e-01
2.11984894e-01 2.05115807e-01 1.99475840e-01 1.93767280e-01
1.83850024e-01 1.81434146e-01 1.72863049e-01 1.67864121e-01
1.61930705e-01 1.58588587e-01 1.54599798e-01 1.43264988e-01
1.41458592e-01 1.34447081e-01 1.26908533e-01 1.22249130e-01
1.20097715e-01 1.16144625e-01 1.11611046e-01 1.04691535e-01
1.01176237e-01 9.96301756e-02 9.57087472e-02 9.46748602e-02
8.88545623e-02 8.64227831e-02 8.36767165e-02 7.94355454e-02
7.84013506e-02 7.08061278e-02 6.97993572e-02 6.35572902e-02
6.30051141e-02 6.15734360e-02 6.01726025e-02 5.71440591e-02
5.61813706e-02 5.57509235e-02 5.27001447e-02 5.17586576e-02
5.00692285e-02 4.92459357e-02 4.78515219e-02 4.61451968e-02
4.37889310e-02 4.33629630e-02 4.24504078e-02 4.01724269e-02
3.95207860e-02 3.88456329e-02 3.82854610e-02 3.72517623e-02
3.70307841e-02 3.51294048e-02 3.50096701e-02 3.43263090e-02
3.35150156e-02 3.26661291e-02 3.22060616e-02 3.16885815e-02
2.99674431e-02 2.95941770e-02 2.86064589e-02 2.82887181e-02
2.76909262e-02 2.73140542e-02 2.67971577e-02 2.56920586e-02
2.53194736e-02 2.46970229e-02 2.43036077e-02 2.37599438e-02
2.36143956e-02 2.28815875e-02 2.25092327e-02 2.22692966e-02
2.18462198e-02 2.13124905e-02 2.06659214e-02 2.05094234e-02
1.99384864e-02 1.93395283e-02 1.88941495e-02 1.86943634e-02
1.78882739e-02 1.76030354e-02 1.71878368e-02 1.68027547e-02
1.63846989e-02 1.62759695e-02 1.52533775e-02 1.50815816e-02
1.48743513e-02 1.45717032e-02 1.39663916e-02 1.32321383e-02
1.31144916e-02 1.26826835e-02 1.24462180e-02 1.21784450e-02
1.20117595e-02 1.15219095e-02 1.11436300e-02 1.07159867e-02
1.02830500e-02 9.75885299e-03 9.69926920e-03 9.37421013e-03
9.15006205e-03 8.76188434e-03 8.35814114e-03 8.15019219e-03
7.93184326e-03 7.58139514e-03 7.47502219e-03 7.31116799e-03
7.15401513e-03 6.93474554e-03 6.70622406e-03 6.68046371e-03
6.44316113e-03 6.27324022e-03 6.17341636e-03 5.78356005e-03
5.53806905e-03 5.40932329e-03 5.17370104e-03 4.88904997e-03
4.86137033e-03 4.45679770e-03 4.18884415e-03 3.96103320e-03
3.86383578e-03 3.59734654e-03 3.53642562e-03 3.31259086e-03
3.17500629e-03 3.15025576e-03 3.05072875e-03 2.79468133e-03
2.66810975e-03 2.56082155e-03 2.46064490e-03 2.36662470e-03
2.21821393e-03 2.13315240e-03 2.06723303e-03 1.94919360e-03
1.84887285e-03 1.80663821e-03 1.72933153e-03 1.62572519e-03
1.48757671e-03 1.46239141e-03 1.39123156e-03 1.34283773e-03
1.22253301e-03 1.13912939e-03 1.05843565e-03 1.00801735e-03
9.70917828e-04 9.22491639e-04 8.91863014e-04 8.73798340e-04
8.23465226e-04 8.10841224e-04 7.73711952e-04 7.61949554e-04
7.25790830e-04 6.89792461e-04 6.75766999e-04 6.41503414e-04
5.78015748e-04 5.64179658e-04 5.47007551e-04 5.09495979e-04
4.52407138e-04 4.35080697e-04 4.02217242e-04 3.75132351e-04
3.61527555e-04 3.19470566e-04 3.13742438e-04 2.90301871e-04
2.60831335e-04 2.56264188e-04 2.37084332e-04 2.31215938e-04
2.26973750e-04 1.91794539e-04 1.86025204e-04 1.78273763e-04
1.67488904e-04 1.47656647e-04 1.40713079e-04 1.27698923e-04
1.19207268e-04 1.18899952e-04 1.12236285e-04 1.06445626e-04
1.00197623e-04 9.17761793e-05 8.75193529e-05 8.52007011e-05
8.22737132e-05 7.01504813e-05 6.77735565e-05 6.17528338e-05
5.76446379e-05 5.07807038e-05 4.84419024e-05 3.35468875e-05
3.20576342e-05 2.80250560e-05 2.31229517e-05 2.12582939e-05
1.94970275e-05 1.74957083e-05 1.57791557e-05 1.36733668e-05
1.06069847e-05 9.50170702e-06 8.73446996e-06 7.29032499e-06
5.14815395e-06 4.61274772e-06 4.12567603e-06 3.70689547e-06
3.55293343e-06 2.64481990e-06 1.65475012e-06 7.19807011e-07
4.11241546e-07 3.23703343e-07 8.52042676e-08 6.79350711e-09
1.95550736e-10 1.69543018e-31 1.10087863e-31 1.10087863e-31
1.10087863e-31 1.10087863e-31]
print('''\n\033[1m''' + '''Eigen Vectors''' + '''\033[0m''')
print(pca.components_)
Eigen Vectors
[[-6.09484203e-03 -1.28123954e-04 -4.09665202e-03 ... 3.45318608e-05
3.30693243e-04 1.53288795e-02]
[-2.47102738e-02 1.26690738e-02 9.17419155e-03 ... 2.02311575e-02
1.64807725e-02 1.68932617e-02]
[-9.55801545e-03 -2.91503461e-03 -7.47617329e-03 ... 3.78052243e-03
4.04450841e-03 -8.27221547e-03]
...
[ 0.00000000e+00 -9.92016288e-17 1.61874131e-16 ... 8.45835030e-17
-7.26179190e-17 7.88532900e-17]
[ 0.00000000e+00 -1.13685586e-16 8.37500564e-17 ... 9.92431377e-17
-1.24375610e-16 -8.75655594e-18]
[ 0.00000000e+00 4.02429304e-17 2.29101309e-17 ... -5.73695968e-17
4.26222758e-17 -2.32159453e-17]]
k = 1
total = []
for i in pca.explained_variance_ratio_*100:
print('Variance explained by Principle Component',k,'is : {:.2f}%'.format(i))
k+=1
total.append(i)
print('\nTotal variance explained by all the principle components:',sum(total),'%')
n=len(total)
Variance explained by Principle Component 1 is : 5.73% Variance explained by Principle Component 2 is : 3.86% Variance explained by Principle Component 3 is : 2.99% Variance explained by Principle Component 4 is : 2.68% Variance explained by Principle Component 5 is : 2.19% Variance explained by Principle Component 6 is : 2.08% Variance explained by Principle Component 7 is : 1.93% Variance explained by Principle Component 8 is : 1.90% Variance explained by Principle Component 9 is : 1.71% Variance explained by Principle Component 10 is : 1.54% Variance explained by Principle Component 11 is : 1.40% Variance explained by Principle Component 12 is : 1.38% Variance explained by Principle Component 13 is : 1.34% Variance explained by Principle Component 14 is : 1.33% Variance explained by Principle Component 15 is : 1.26% Variance explained by Principle Component 16 is : 1.21% Variance explained by Principle Component 17 is : 1.20% Variance explained by Principle Component 18 is : 1.16% Variance explained by Principle Component 19 is : 1.12% Variance explained by Principle Component 20 is : 1.08% Variance explained by Principle Component 21 is : 1.06% Variance explained by Principle Component 22 is : 1.03% Variance explained by Principle Component 23 is : 1.01% Variance explained by Principle Component 24 is : 0.99% Variance explained by Principle Component 25 is : 0.98% Variance explained by Principle Component 26 is : 0.97% Variance explained by Principle Component 27 is : 0.91% Variance explained by Principle Component 28 is : 0.91% Variance explained by Principle Component 29 is : 0.88% Variance explained by Principle Component 30 is : 0.86% Variance explained by Principle Component 31 is : 0.86% Variance explained by Principle Component 32 is : 0.83% Variance explained by Principle Component 33 is : 0.82% Variance explained by Principle Component 34 is : 0.80% Variance explained by Principle Component 35 is : 0.80% Variance explained by Principle Component 36 is : 0.78% Variance explained by Principle Component 37 is : 0.77% Variance explained by Principle Component 38 is : 0.74% Variance explained by Principle Component 39 is : 0.74% Variance explained by Principle Component 40 is : 0.72% Variance explained by Principle Component 41 is : 0.71% Variance explained by Principle Component 42 is : 0.70% Variance explained by Principle Component 43 is : 0.69% Variance explained by Principle Component 44 is : 0.69% Variance explained by Principle Component 45 is : 0.67% Variance explained by Principle Component 46 is : 0.65% Variance explained by Principle Component 47 is : 0.64% Variance explained by Principle Component 48 is : 0.64% Variance explained by Principle Component 49 is : 0.63% Variance explained by Principle Component 50 is : 0.61% Variance explained by Principle Component 51 is : 0.60% Variance explained by Principle Component 52 is : 0.59% Variance explained by Principle Component 53 is : 0.58% Variance explained by Principle Component 54 is : 0.58% Variance explained by Principle Component 55 is : 0.57% Variance explained by Principle Component 56 is : 0.56% Variance explained by Principle Component 57 is : 0.55% Variance explained by Principle Component 58 is : 0.54% Variance explained by Principle Component 59 is : 0.54% Variance explained by Principle Component 60 is : 0.53% Variance explained by Principle Component 61 is : 0.51% Variance explained by Principle Component 62 is : 0.51% Variance explained by Principle Component 63 is : 0.50% Variance explained by Principle Component 64 is : 0.50% Variance explained by Principle Component 65 is : 0.49% Variance explained by Principle Component 66 is : 0.48% Variance explained by Principle Component 67 is : 0.47% Variance explained by Principle Component 68 is : 0.46% Variance explained by Principle Component 69 is : 0.46% Variance explained by Principle Component 70 is : 0.45% Variance explained by Principle Component 71 is : 0.45% Variance explained by Principle Component 72 is : 0.44% Variance explained by Principle Component 73 is : 0.43% Variance explained by Principle Component 74 is : 0.41% Variance explained by Principle Component 75 is : 0.40% Variance explained by Principle Component 76 is : 0.40% Variance explained by Principle Component 77 is : 0.38% Variance explained by Principle Component 78 is : 0.38% Variance explained by Principle Component 79 is : 0.38% Variance explained by Principle Component 80 is : 0.37% Variance explained by Principle Component 81 is : 0.36% Variance explained by Principle Component 82 is : 0.35% Variance explained by Principle Component 83 is : 0.35% Variance explained by Principle Component 84 is : 0.34% Variance explained by Principle Component 85 is : 0.33% Variance explained by Principle Component 86 is : 0.33% Variance explained by Principle Component 87 is : 0.32% Variance explained by Principle Component 88 is : 0.31% Variance explained by Principle Component 89 is : 0.31% Variance explained by Principle Component 90 is : 0.30% Variance explained by Principle Component 91 is : 0.30% Variance explained by Principle Component 92 is : 0.30% Variance explained by Principle Component 93 is : 0.29% Variance explained by Principle Component 94 is : 0.28% Variance explained by Principle Component 95 is : 0.28% Variance explained by Principle Component 96 is : 0.27% Variance explained by Principle Component 97 is : 0.27% Variance explained by Principle Component 98 is : 0.27% Variance explained by Principle Component 99 is : 0.26% Variance explained by Principle Component 100 is : 0.26% Variance explained by Principle Component 101 is : 0.26% Variance explained by Principle Component 102 is : 0.26% Variance explained by Principle Component 103 is : 0.25% Variance explained by Principle Component 104 is : 0.25% Variance explained by Principle Component 105 is : 0.24% Variance explained by Principle Component 106 is : 0.24% Variance explained by Principle Component 107 is : 0.24% Variance explained by Principle Component 108 is : 0.23% Variance explained by Principle Component 109 is : 0.23% Variance explained by Principle Component 110 is : 0.22% Variance explained by Principle Component 111 is : 0.22% Variance explained by Principle Component 112 is : 0.22% Variance explained by Principle Component 113 is : 0.22% Variance explained by Principle Component 114 is : 0.21% Variance explained by Principle Component 115 is : 0.21% Variance explained by Principle Component 116 is : 0.21% Variance explained by Principle Component 117 is : 0.21% Variance explained by Principle Component 118 is : 0.21% Variance explained by Principle Component 119 is : 0.20% Variance explained by Principle Component 120 is : 0.20% Variance explained by Principle Component 121 is : 0.20% Variance explained by Principle Component 122 is : 0.20% Variance explained by Principle Component 123 is : 0.20% Variance explained by Principle Component 124 is : 0.19% Variance explained by Principle Component 125 is : 0.19% Variance explained by Principle Component 126 is : 0.19% Variance explained by Principle Component 127 is : 0.19% Variance explained by Principle Component 128 is : 0.19% Variance explained by Principle Component 129 is : 0.19% Variance explained by Principle Component 130 is : 0.18% Variance explained by Principle Component 131 is : 0.18% Variance explained by Principle Component 132 is : 0.18% Variance explained by Principle Component 133 is : 0.18% Variance explained by Principle Component 134 is : 0.17% Variance explained by Principle Component 135 is : 0.17% Variance explained by Principle Component 136 is : 0.17% Variance explained by Principle Component 137 is : 0.17% Variance explained by Principle Component 138 is : 0.17% Variance explained by Principle Component 139 is : 0.16% Variance explained by Principle Component 140 is : 0.16% Variance explained by Principle Component 141 is : 0.16% Variance explained by Principle Component 142 is : 0.16% Variance explained by Principle Component 143 is : 0.16% Variance explained by Principle Component 144 is : 0.15% Variance explained by Principle Component 145 is : 0.15% Variance explained by Principle Component 146 is : 0.15% Variance explained by Principle Component 147 is : 0.15% Variance explained by Principle Component 148 is : 0.15% Variance explained by Principle Component 149 is : 0.14% Variance explained by Principle Component 150 is : 0.14% Variance explained by Principle Component 151 is : 0.14% Variance explained by Principle Component 152 is : 0.14% Variance explained by Principle Component 153 is : 0.14% Variance explained by Principle Component 154 is : 0.14% Variance explained by Principle Component 155 is : 0.13% Variance explained by Principle Component 156 is : 0.13% Variance explained by Principle Component 157 is : 0.13% Variance explained by Principle Component 158 is : 0.13% Variance explained by Principle Component 159 is : 0.13% Variance explained by Principle Component 160 is : 0.13% Variance explained by Principle Component 161 is : 0.13% Variance explained by Principle Component 162 is : 0.12% Variance explained by Principle Component 163 is : 0.12% Variance explained by Principle Component 164 is : 0.12% Variance explained by Principle Component 165 is : 0.12% Variance explained by Principle Component 166 is : 0.11% Variance explained by Principle Component 167 is : 0.11% Variance explained by Principle Component 168 is : 0.11% Variance explained by Principle Component 169 is : 0.11% Variance explained by Principle Component 170 is : 0.11% Variance explained by Principle Component 171 is : 0.11% Variance explained by Principle Component 172 is : 0.11% Variance explained by Principle Component 173 is : 0.10% Variance explained by Principle Component 174 is : 0.10% Variance explained by Principle Component 175 is : 0.10% Variance explained by Principle Component 176 is : 0.10% Variance explained by Principle Component 177 is : 0.10% Variance explained by Principle Component 178 is : 0.10% Variance explained by Principle Component 179 is : 0.09% Variance explained by Principle Component 180 is : 0.09% Variance explained by Principle Component 181 is : 0.09% Variance explained by Principle Component 182 is : 0.09% Variance explained by Principle Component 183 is : 0.08% Variance explained by Principle Component 184 is : 0.08% Variance explained by Principle Component 185 is : 0.08% Variance explained by Principle Component 186 is : 0.08% Variance explained by Principle Component 187 is : 0.08% Variance explained by Principle Component 188 is : 0.08% Variance explained by Principle Component 189 is : 0.07% Variance explained by Principle Component 190 is : 0.07% Variance explained by Principle Component 191 is : 0.07% Variance explained by Principle Component 192 is : 0.07% Variance explained by Principle Component 193 is : 0.07% Variance explained by Principle Component 194 is : 0.06% Variance explained by Principle Component 195 is : 0.06% Variance explained by Principle Component 196 is : 0.06% Variance explained by Principle Component 197 is : 0.06% Variance explained by Principle Component 198 is : 0.06% Variance explained by Principle Component 199 is : 0.06% Variance explained by Principle Component 200 is : 0.05% Variance explained by Principle Component 201 is : 0.05% Variance explained by Principle Component 202 is : 0.05% Variance explained by Principle Component 203 is : 0.05% Variance explained by Principle Component 204 is : 0.05% Variance explained by Principle Component 205 is : 0.05% Variance explained by Principle Component 206 is : 0.05% Variance explained by Principle Component 207 is : 0.04% Variance explained by Principle Component 208 is : 0.04% Variance explained by Principle Component 209 is : 0.04% Variance explained by Principle Component 210 is : 0.04% Variance explained by Principle Component 211 is : 0.04% Variance explained by Principle Component 212 is : 0.04% Variance explained by Principle Component 213 is : 0.04% Variance explained by Principle Component 214 is : 0.04% Variance explained by Principle Component 215 is : 0.03% Variance explained by Principle Component 216 is : 0.03% Variance explained by Principle Component 217 is : 0.03% Variance explained by Principle Component 218 is : 0.03% Variance explained by Principle Component 219 is : 0.03% Variance explained by Principle Component 220 is : 0.03% Variance explained by Principle Component 221 is : 0.03% Variance explained by Principle Component 222 is : 0.03% Variance explained by Principle Component 223 is : 0.03% Variance explained by Principle Component 224 is : 0.02% Variance explained by Principle Component 225 is : 0.02% Variance explained by Principle Component 226 is : 0.02% Variance explained by Principle Component 227 is : 0.02% Variance explained by Principle Component 228 is : 0.02% Variance explained by Principle Component 229 is : 0.02% Variance explained by Principle Component 230 is : 0.02% Variance explained by Principle Component 231 is : 0.02% Variance explained by Principle Component 232 is : 0.02% Variance explained by Principle Component 233 is : 0.02% Variance explained by Principle Component 234 is : 0.02% Variance explained by Principle Component 235 is : 0.02% Variance explained by Principle Component 236 is : 0.01% Variance explained by Principle Component 237 is : 0.01% Variance explained by Principle Component 238 is : 0.01% Variance explained by Principle Component 239 is : 0.01% Variance explained by Principle Component 240 is : 0.01% Variance explained by Principle Component 241 is : 0.01% Variance explained by Principle Component 242 is : 0.01% Variance explained by Principle Component 243 is : 0.01% Variance explained by Principle Component 244 is : 0.01% Variance explained by Principle Component 245 is : 0.01% Variance explained by Principle Component 246 is : 0.01% Variance explained by Principle Component 247 is : 0.01% Variance explained by Principle Component 248 is : 0.01% Variance explained by Principle Component 249 is : 0.01% Variance explained by Principle Component 250 is : 0.01% Variance explained by Principle Component 251 is : 0.01% Variance explained by Principle Component 252 is : 0.01% Variance explained by Principle Component 253 is : 0.01% Variance explained by Principle Component 254 is : 0.01% Variance explained by Principle Component 255 is : 0.01% Variance explained by Principle Component 256 is : 0.01% Variance explained by Principle Component 257 is : 0.01% Variance explained by Principle Component 258 is : 0.01% Variance explained by Principle Component 259 is : 0.01% Variance explained by Principle Component 260 is : 0.01% Variance explained by Principle Component 261 is : 0.01% Variance explained by Principle Component 262 is : 0.01% Variance explained by Principle Component 263 is : 0.01% Variance explained by Principle Component 264 is : 0.01% Variance explained by Principle Component 265 is : 0.01% Variance explained by Principle Component 266 is : 0.01% Variance explained by Principle Component 267 is : 0.01% Variance explained by Principle Component 268 is : 0.01% Variance explained by Principle Component 269 is : 0.01% Variance explained by Principle Component 270 is : 0.01% Variance explained by Principle Component 271 is : 0.01% Variance explained by Principle Component 272 is : 0.01% Variance explained by Principle Component 273 is : 0.01% Variance explained by Principle Component 274 is : 0.01% Variance explained by Principle Component 275 is : 0.01% Variance explained by Principle Component 276 is : 0.01% Variance explained by Principle Component 277 is : 0.01% Variance explained by Principle Component 278 is : 0.01% Variance explained by Principle Component 279 is : 0.01% Variance explained by Principle Component 280 is : 0.00% Variance explained by Principle Component 281 is : 0.00% Variance explained by Principle Component 282 is : 0.00% Variance explained by Principle Component 283 is : 0.00% Variance explained by Principle Component 284 is : 0.00% Variance explained by Principle Component 285 is : 0.00% Variance explained by Principle Component 286 is : 0.00% Variance explained by Principle Component 287 is : 0.00% Variance explained by Principle Component 288 is : 0.00% Variance explained by Principle Component 289 is : 0.00% Variance explained by Principle Component 290 is : 0.00% Variance explained by Principle Component 291 is : 0.00% Variance explained by Principle Component 292 is : 0.00% Variance explained by Principle Component 293 is : 0.00% Variance explained by Principle Component 294 is : 0.00% Variance explained by Principle Component 295 is : 0.00% Variance explained by Principle Component 296 is : 0.00% Variance explained by Principle Component 297 is : 0.00% Variance explained by Principle Component 298 is : 0.00% Variance explained by Principle Component 299 is : 0.00% Variance explained by Principle Component 300 is : 0.00% Variance explained by Principle Component 301 is : 0.00% Variance explained by Principle Component 302 is : 0.00% Variance explained by Principle Component 303 is : 0.00% Variance explained by Principle Component 304 is : 0.00% Variance explained by Principle Component 305 is : 0.00% Variance explained by Principle Component 306 is : 0.00% Variance explained by Principle Component 307 is : 0.00% Variance explained by Principle Component 308 is : 0.00% Variance explained by Principle Component 309 is : 0.00% Variance explained by Principle Component 310 is : 0.00% Variance explained by Principle Component 311 is : 0.00% Variance explained by Principle Component 312 is : 0.00% Variance explained by Principle Component 313 is : 0.00% Variance explained by Principle Component 314 is : 0.00% Variance explained by Principle Component 315 is : 0.00% Variance explained by Principle Component 316 is : 0.00% Variance explained by Principle Component 317 is : 0.00% Variance explained by Principle Component 318 is : 0.00% Variance explained by Principle Component 319 is : 0.00% Variance explained by Principle Component 320 is : 0.00% Variance explained by Principle Component 321 is : 0.00% Variance explained by Principle Component 322 is : 0.00% Variance explained by Principle Component 323 is : 0.00% Variance explained by Principle Component 324 is : 0.00% Variance explained by Principle Component 325 is : 0.00% Variance explained by Principle Component 326 is : 0.00% Variance explained by Principle Component 327 is : 0.00% Variance explained by Principle Component 328 is : 0.00% Variance explained by Principle Component 329 is : 0.00% Variance explained by Principle Component 330 is : 0.00% Variance explained by Principle Component 331 is : 0.00% Variance explained by Principle Component 332 is : 0.00% Variance explained by Principle Component 333 is : 0.00% Variance explained by Principle Component 334 is : 0.00% Variance explained by Principle Component 335 is : 0.00% Variance explained by Principle Component 336 is : 0.00% Variance explained by Principle Component 337 is : 0.00% Variance explained by Principle Component 338 is : 0.00% Variance explained by Principle Component 339 is : 0.00% Variance explained by Principle Component 340 is : 0.00% Variance explained by Principle Component 341 is : 0.00% Variance explained by Principle Component 342 is : 0.00% Variance explained by Principle Component 343 is : 0.00% Variance explained by Principle Component 344 is : 0.00% Variance explained by Principle Component 345 is : 0.00% Variance explained by Principle Component 346 is : 0.00% Variance explained by Principle Component 347 is : 0.00% Variance explained by Principle Component 348 is : 0.00% Variance explained by Principle Component 349 is : 0.00% Variance explained by Principle Component 350 is : 0.00% Variance explained by Principle Component 351 is : 0.00% Variance explained by Principle Component 352 is : 0.00% Variance explained by Principle Component 353 is : 0.00% Variance explained by Principle Component 354 is : 0.00% Variance explained by Principle Component 355 is : 0.00% Variance explained by Principle Component 356 is : 0.00% Variance explained by Principle Component 357 is : 0.00% Variance explained by Principle Component 358 is : 0.00% Variance explained by Principle Component 359 is : 0.00% Variance explained by Principle Component 360 is : 0.00% Variance explained by Principle Component 361 is : 0.00% Variance explained by Principle Component 362 is : 0.00% Variance explained by Principle Component 363 is : 0.00% Variance explained by Principle Component 364 is : 0.00% Variance explained by Principle Component 365 is : 0.00% Variance explained by Principle Component 366 is : 0.00% Variance explained by Principle Component 367 is : 0.00% Variance explained by Principle Component 368 is : 0.00% Variance explained by Principle Component 369 is : 0.00% Variance explained by Principle Component 370 is : 0.00% Variance explained by Principle Component 371 is : 0.00% Variance explained by Principle Component 372 is : 0.00% Variance explained by Principle Component 373 is : 0.00% Variance explained by Principle Component 374 is : 0.00% Variance explained by Principle Component 375 is : 0.00% Variance explained by Principle Component 376 is : 0.00% Variance explained by Principle Component 377 is : 0.00% Variance explained by Principle Component 378 is : 0.00% Variance explained by Principle Component 379 is : 0.00% Variance explained by Principle Component 380 is : 0.00% Variance explained by Principle Component 381 is : 0.00% Variance explained by Principle Component 382 is : 0.00% Variance explained by Principle Component 383 is : 0.00% Variance explained by Principle Component 384 is : 0.00% Variance explained by Principle Component 385 is : 0.00% Variance explained by Principle Component 386 is : 0.00% Variance explained by Principle Component 387 is : 0.00% Variance explained by Principle Component 388 is : 0.00% Variance explained by Principle Component 389 is : 0.00% Variance explained by Principle Component 390 is : 0.00% Variance explained by Principle Component 391 is : 0.00% Variance explained by Principle Component 392 is : 0.00% Variance explained by Principle Component 393 is : 0.00% Variance explained by Principle Component 394 is : 0.00% Variance explained by Principle Component 395 is : 0.00% Variance explained by Principle Component 396 is : 0.00% Variance explained by Principle Component 397 is : 0.00% Variance explained by Principle Component 398 is : 0.00% Variance explained by Principle Component 399 is : 0.00% Variance explained by Principle Component 400 is : 0.00% Variance explained by Principle Component 401 is : 0.00% Variance explained by Principle Component 402 is : 0.00% Variance explained by Principle Component 403 is : 0.00% Variance explained by Principle Component 404 is : 0.00% Variance explained by Principle Component 405 is : 0.00% Variance explained by Principle Component 406 is : 0.00% Variance explained by Principle Component 407 is : 0.00% Variance explained by Principle Component 408 is : 0.00% Variance explained by Principle Component 409 is : 0.00% Variance explained by Principle Component 410 is : 0.00% Variance explained by Principle Component 411 is : 0.00% Variance explained by Principle Component 412 is : 0.00% Variance explained by Principle Component 413 is : 0.00% Variance explained by Principle Component 414 is : 0.00% Variance explained by Principle Component 415 is : 0.00% Variance explained by Principle Component 416 is : 0.00% Variance explained by Principle Component 417 is : 0.00% Variance explained by Principle Component 418 is : 0.00% Variance explained by Principle Component 419 is : 0.00% Variance explained by Principle Component 420 is : 0.00% Variance explained by Principle Component 421 is : 0.00% Variance explained by Principle Component 422 is : 0.00% Variance explained by Principle Component 423 is : 0.00% Variance explained by Principle Component 424 is : 0.00% Variance explained by Principle Component 425 is : 0.00% Variance explained by Principle Component 426 is : 0.00% Variance explained by Principle Component 427 is : 0.00% Variance explained by Principle Component 428 is : 0.00% Variance explained by Principle Component 429 is : 0.00% Variance explained by Principle Component 430 is : 0.00% Variance explained by Principle Component 431 is : 0.00% Variance explained by Principle Component 432 is : 0.00% Variance explained by Principle Component 433 is : 0.00% Variance explained by Principle Component 434 is : 0.00% Variance explained by Principle Component 435 is : 0.00% Variance explained by Principle Component 436 is : 0.00% Variance explained by Principle Component 437 is : 0.00% Variance explained by Principle Component 438 is : 0.00% Variance explained by Principle Component 439 is : 0.00% Variance explained by Principle Component 440 is : 0.00% Variance explained by Principle Component 441 is : 0.00% Variance explained by Principle Component 442 is : 0.00% Variance explained by Principle Component 443 is : 0.00% Variance explained by Principle Component 444 is : 0.00% Variance explained by Principle Component 445 is : 0.00% Variance explained by Principle Component 446 is : 0.00% Total variance explained by all the principle components: 100.00000000000009 %
print('''\n\033[1m''' + '''Graph Visualization''' + '''\033[0m''')
plt.figure(figsize=(10 ,3))
plt.bar(range(1, n+1), pca.explained_variance_ratio_, label = 'Individual explained variance',color='lightblue',edgecolor='black')
plt.step(range(1, n+1), np.cumsum(pca.explained_variance_ratio_),where='mid', label = 'Cumulative explained variance')
plt.ylabel('Explained Variance Ratio')
plt.xlabel('Principal Components')
plt.legend(loc = 'best')
plt.show()
Graph Visualization
print('''\n\033[1m''' + '''Selecting Components having percentage more than 0''' + '''\033[0m''')
n=[]
for s in total:
if s>1:
n.append(s)
print('Total Non-Zero Percentage of variance explained by Principle Component',len(n),'\nWhich are:\n',n)
Selecting Components having percentage more than 0
Total Non-Zero Percentage of variance explained by Principle Component 23
Which are:
[5.72808849008744, 3.856293984929741, 2.988817618873338, 2.676911079306572, 2.1934449636267894, 2.0792406741628904, 1.930090560270093, 1.898798204800847, 1.705594872489182, 1.5383832271819162, 1.4049619919773928, 1.38098395184282, 1.3441134304432878, 1.3283875849882096, 1.2603416933208036, 1.205608468366907, 1.1961783656239269, 1.1592001770457228, 1.1155446188532157, 1.0799099565682801, 1.061290942277238, 1.0345942312184155, 1.0062535335289227]
n=len(n)
pca2 = PCA(n_components=n)
pca2.fit(x)
PCA(n_components=23)
print('''\n\033[1m''' + '''PCA Dataframe''' + '''\033[0m''')
pd.DataFrame(pca2.components_)
PCA Dataframe
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 358 | 359 | 360 | 361 | 362 | 363 | 364 | 365 | 366 | 367 | 368 | 369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 | 379 | 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | 402 | 403 | 404 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 414 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 422 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.006095 | -0.000128 | -0.004097 | 0.002546 | 0.001019 | 0.000458 | 0.002081 | -0.006160 | -0.005154 | 0.000942 | -0.000747 | -0.004467 | 0.006399 | 0.017587 | 0.002433 | 0.002221 | -0.007407 | 0.003093 | -0.000899 | -0.007197 | 0.008009 | -0.008942 | 0.000287 | 0.010335 | 0.011978 | 0.011258 | 0.006579 | -0.002982 | -0.003554 | -0.001896 | 0.021154 | 0.019789 | 0.013809 | 0.007158 | -0.013809 | 0.009597 | -0.005672 | 0.006088 | -0.002306 | -0.004646 | 0.005286 | -0.009297 | 0.021300 | 0.022427 | 0.005780 | -0.004199 | 0.020956 | 0.007625 | -0.000539 | -0.001407 | 0.001189 | -0.011890 | 0.002464 | -0.023454 | 0.072604 | -0.018152 | 0.011328 | 0.105872 | 0.103517 | 0.026367 | 0.067645 | 0.009118 | 0.162464 | -0.078486 | 0.050546 | -0.005488 | 0.103128 | 0.010483 | -0.002618 | -0.004855 | -0.006510 | -0.007897 | 0.000413 | -0.004098 | -0.004938 | 0.001907 | 0.002474 | 0.005816 | 0.009735 | -0.001138 | -0.002986 | -0.002853 | 0.000376 | 0.003748 | 0.004856 | 0.004933 | -0.001296 | -0.004256 | -0.003785 | -0.007098 | 0.015755 | -0.003083 | -0.004102 | -0.001032 | 0.007552 | -0.003928 | -0.004671 | -0.005937 | -0.003883 | 0.001955 | 0.002246 | -0.001954 | 0.000831 | -0.017755 | -0.000534 | 0.001838 | 0.007510 | -0.004242 | -0.000088 | 0.004250 | -0.001438 | 0.005170 | 0.004726 | -0.004637 | 0.001562 | -0.007484 | 0.002184 | -0.007089 | 0.003795 | -0.011241 | -0.012563 | -0.006217 | 0.001802 | -0.012862 | -0.017121 | -0.011795 | 0.003796 | 0.001006 | 0.002883 | 0.003013 | 0.006192 | -0.006342 | -0.003933 | 0.000605 | 0.001728 | -0.004129 | 0.047595 | 0.000921 | -0.002087 | 0.003956 | -0.004757 | 0.003385 | -0.012042 | -0.009969 | -0.001093 | -0.001767 | -0.012372 | -0.013033 | -0.013468 | -0.001659 | 0.007663 | 0.006193 | 0.008755 | -0.004615 | 0.001559 | -0.012074 | -0.010182 | -0.012078 | -0.008571 | 0.001869 | -0.003464 | 0.009003 | 0.006364 | 0.033717 | 0.015464 | 0.007939 | 0.007321 | 0.011788 | 0.006149 | -0.008751 | 0.188616 | 0.173660 | 0.149984 | 0.182682 | 0.139075 | 0.115887 | 0.159673 | 0.177118 | 0.178991 | 0.170136 | 0.103128 | 0.187098 | 0.011301 | 0.103128 | -0.006683 | -0.001447 | -0.006123 | -0.002383 | -0.003417 | 0.004084 | -0.004511 | -0.000390 | -0.003273 | 0.001574 | 0.003957 | -0.001541 | -0.002681 | 0.002330 | -0.003340 | -0.001872 | 0.006286 | -0.000182 | -0.001087 | -0.005594 | 0.000067 | -0.001616 | -0.002988 | 0.062237 | 0.000549 | 0.001248 | -0.007003 | -0.000600 | -0.009820 | 0.016191 | -0.006776 | 0.003726 | -0.013596 | -0.017552 | -0.012604 | 0.004002 | 0.001006 | 0.002385 | 0.003891 | 0.007795 | -0.004862 | -0.003020 | 0.000767 | 0.001701 | -0.004692 | 0.059339 | 0.000915 | -0.001956 | 0.003570 | -0.004283 | 0.003711 | -0.012601 | -0.010299 | -0.001843 | -0.002028 | -0.012675 | -0.013394 | -0.013473 | -0.004091 | 0.009160 | 0.008717 | 0.008227 | -0.003036 | 0.001996 | -0.010633 | -0.009532 | -0.010644 | -0.007222 | 0.002472 | -0.003618 | 0.009964 | 0.005861 | 0.033982 | 0.011729 | 0.007195 | 0.007284 | 0.009470 | 0.004874 | -0.007805 | 0.177791 | 0.168787 | 0.156376 | 0.175704 | 0.175555 | 0.134267 | 0.159829 | 0.170677 | 0.170989 | 0.170953 | 0.103128 | 0.179977 | 0.014722 | 0.103128 | -0.007161 | 0.001532 | -0.006516 | -0.002682 | -0.004544 | 0.004936 | -0.003290 | -0.000335 | -0.004476 | -0.000204 | 0.003303 | -0.001530 | 0.000808 | 0.002264 | -0.004217 | -0.003255 | 0.004045 | 0.001581 | 0.004493 | -0.000679 | 0.000157 | -0.003205 | -0.000030 | -0.001638 | -0.003997 | 0.062395 | 0.000489 | 0.000117 | -0.007183 | -0.002073 | -0.010234 | 0.017007 | -0.007088 | 0.001832 | -0.012760 | -0.016927 | -0.011727 | 0.001831 | -0.000535 | 0.002891 | 0.002469 | 0.007376 | 0.001694 | 0.004064 | 0.000629 | 0.002071 | -0.010508 | 0.046050 | 0.000361 | -0.002158 | 0.003897 | -0.004244 | 0.003504 | -0.012645 | -0.012318 | -0.002285 | -0.000591 | -0.012328 | -0.012195 | -0.012513 | -0.002473 | 0.005098 | 0.004454 | 0.008242 | -0.006754 | -0.002267 | -0.012588 | -0.010860 | -0.011342 | -0.008869 | 0.001899 | -0.003536 | 0.008623 | 0.006595 | 0.030721 | 0.014096 | 0.006974 | 0.007655 | 0.011352 | -0.002564 | -0.008382 | 0.009629 | 0.172938 | 0.146169 | 0.168580 | 0.037684 | 0.081486 | 0.135189 | 0.173411 | 0.087275 | 0.170247 | 0.103128 | 0.187915 | 0.016322 | 0.002411 | -0.005610 | 0.003540 | 0.006371 | 0.003230 | -0.001797 | -0.007525 | 0.001929 | -0.003520 | 0.001498 | 0.003747 | -0.001466 | -0.002507 | 0.006764 | -0.003157 | 0.000704 | -0.008187 | 0.001621 | 0.012080 | -0.005481 | 0.000043 | 0.002056 | -0.003414 | 0.062021 | 0.003114 | 0.001057 | -0.006977 | -0.000282 | -0.009247 | 0.016448 | -0.005632 | -0.004874 | 0.005322 | -0.000760 | 0.006503 | 0.000086 | 0.000905 | -0.009147 | -0.002061 | 0.000836 | 0.003120 | -0.002293 | 0.000904 | 0.002206 | -0.002945 | 0.000834 | 0.003420 | -0.000473 | 0.006324 | 0.004684 | 0.007131 | 0.002353 | 0.001428 | -0.002794 | -0.002007 | -0.003397 | -0.001632 | -0.003048 | -0.002865 | 0.001637 | -0.003448 | 0.006534 | 0.012629 | 0.005471 | 0.012400 | 0.006054 | 0.010728 | -0.008015 | 0.003260 | 0.003586 | 0.003303 | -0.007057 | 0.000035 | 0.000331 | 0.015329 |
| 1 | -0.024710 | 0.012669 | 0.009174 | -0.032670 | -0.008608 | 0.036899 | 0.007558 | 0.036399 | -0.018416 | 0.018525 | -0.050985 | 0.013063 | 0.002009 | -0.002413 | 0.027093 | -0.032166 | -0.008015 | 0.001549 | -0.002405 | 0.095094 | -0.088986 | 0.042238 | 0.022415 | -0.180490 | -0.188210 | -0.176174 | -0.108524 | 0.043309 | 0.045363 | 0.047598 | 0.006741 | 0.011220 | -0.003642 | -0.002203 | 0.003642 | 0.022635 | 0.087508 | 0.005370 | -0.034112 | 0.012886 | 0.012900 | -0.028991 | -0.017245 | -0.011587 | -0.002697 | 0.005975 | -0.003234 | 0.016973 | 0.006827 | 0.002384 | 0.035365 | 0.027331 | 0.012564 | 0.031716 | 0.073827 | -0.001271 | 0.007677 | -0.022320 | 0.053913 | 0.035457 | 0.033472 | -0.030227 | 0.024014 | -0.018971 | -0.029490 | -0.038315 | -0.004037 | -0.048304 | -0.037887 | 0.000658 | -0.084177 | 0.083104 | -0.022687 | 0.000861 | -0.015038 | -0.026260 | 0.004423 | -0.017048 | -0.013387 | 0.004345 | 0.039391 | -0.012453 | -0.000141 | 0.008033 | 0.016392 | -0.003170 | 0.032849 | -0.004727 | 0.007006 | 0.001041 | -0.001226 | 0.003784 | 0.003127 | 0.084455 | 0.000721 | -0.008338 | -0.016563 | 0.002092 | -0.001922 | 0.012338 | -0.029514 | 0.021587 | -0.009699 | -0.014236 | -0.004826 | -0.009193 | -0.015021 | 0.009897 | 0.021258 | -0.035147 | 0.020683 | 0.040366 | -0.055407 | -0.001900 | -0.037748 | 0.003162 | 0.056854 | 0.021732 | -0.001368 | 0.044264 | 0.009820 | 0.001095 | -0.007607 | 0.017240 | 0.013943 | 0.030967 | -0.035788 | -0.008478 | -0.003533 | 0.024736 | -0.034597 | -0.003179 | -0.000667 | 0.038401 | 0.024494 | 0.018280 | -0.006622 | 0.024481 | 0.035967 | 0.026181 | -0.000150 | -0.002708 | 0.168391 | 0.156501 | 0.005820 | -0.056602 | 0.187123 | 0.201790 | 0.195341 | -0.024364 | -0.059605 | -0.061310 | -0.112985 | 0.026175 | -0.026764 | -0.001138 | -0.025913 | -0.001127 | -0.013731 | -0.029798 | 0.065961 | -0.065692 | -0.024060 | -0.029866 | 0.005156 | -0.021603 | -0.016736 | -0.015979 | 0.006251 | 0.020253 | 0.011891 | 0.009611 | 0.012106 | 0.008992 | 0.008752 | -0.020330 | -0.005831 | 0.005090 | 0.019616 | 0.029024 | -0.004037 | 0.014151 | 0.031695 | -0.004037 | 0.028217 | 0.011590 | 0.021262 | 0.036188 | -0.000312 | -0.015040 | -0.000164 | -0.000871 | 0.002110 | 0.007605 | 0.023084 | 0.004506 | -0.013704 | 0.035292 | 0.008137 | 0.023110 | 0.017401 | 0.015569 | -0.019238 | 0.056229 | 0.016916 | 0.021114 | -0.001254 | 0.012625 | -0.003631 | 0.031394 | 0.021038 | 0.028132 | -0.051167 | 0.022741 | -0.022904 | -0.007986 | 0.025304 | 0.022528 | 0.023133 | -0.036512 | -0.008481 | -0.004131 | 0.023292 | -0.034750 | -0.009942 | 0.004470 | 0.038996 | 0.024172 | 0.016275 | -0.007174 | 0.024433 | 0.036956 | 0.025431 | 0.003349 | -0.004502 | 0.176536 | 0.161023 | 0.009479 | -0.056499 | 0.191894 | 0.202070 | 0.197044 | -0.025227 | -0.059830 | -0.061706 | -0.112531 | 0.013929 | -0.027185 | -0.004583 | -0.016224 | -0.004587 | -0.028253 | -0.031512 | 0.067399 | -0.070785 | -0.023061 | -0.037116 | 0.004870 | -0.020927 | -0.016480 | -0.017382 | 0.007239 | 0.014987 | 0.015249 | 0.012199 | 0.016216 | 0.012844 | 0.011496 | -0.012591 | -0.002808 | 0.000420 | 0.021558 | 0.028132 | -0.004037 | 0.014080 | 0.031438 | -0.004037 | 0.030660 | 0.010562 | 0.022167 | 0.037207 | 0.001325 | -0.016789 | -0.001329 | -0.001075 | 0.001929 | 0.007948 | 0.021720 | 0.004315 | -0.014956 | 0.035383 | 0.013871 | 0.026156 | 0.021168 | 0.008924 | 0.020471 | 0.015120 | -0.016465 | 0.039362 | 0.016405 | 0.021014 | 0.003704 | 0.012537 | -0.003576 | 0.026341 | 0.023866 | 0.034850 | -0.049533 | 0.023336 | -0.021979 | -0.008531 | 0.019457 | 0.012803 | 0.031007 | -0.015481 | -0.000694 | -0.009013 | 0.026156 | -0.038868 | -0.010845 | 0.005447 | 0.037926 | 0.024715 | 0.018172 | -0.006855 | 0.032883 | 0.036093 | 0.026105 | -0.001626 | -0.002343 | 0.194367 | 0.198450 | 0.031198 | -0.020506 | 0.200786 | 0.199481 | 0.200728 | -0.010152 | -0.059572 | -0.062414 | -0.110839 | 0.025835 | -0.030645 | -0.000397 | -0.024755 | -0.001771 | -0.014145 | -0.030700 | 0.066259 | -0.067214 | -0.022580 | -0.027546 | 0.005448 | -0.022222 | -0.017563 | -0.016049 | -0.013307 | 0.019835 | -0.042627 | 0.010566 | 0.011925 | 0.002783 | -0.035472 | -0.034632 | -0.027019 | 0.004821 | 0.043721 | 0.028536 | -0.004037 | 0.013855 | 0.054221 | 0.000819 | -0.020380 | 0.017101 | -0.005270 | 0.003323 | -0.029677 | 0.003872 | -0.008022 | 0.006790 | 0.006738 | 0.023564 | 0.004110 | -0.013035 | 0.007092 | 0.009386 | -0.005053 | 0.005832 | 0.069519 | -0.000392 | 0.052427 | 0.016162 | 0.010730 | 0.000401 | 0.012686 | -0.001856 | 0.031738 | 0.021087 | 0.028017 | -0.054111 | 0.022594 | -0.024536 | -0.069040 | -0.050930 | 0.035905 | -0.043761 | 0.009006 | 0.037413 | 0.055252 | 0.026134 | 0.032929 | 0.012646 | 0.025558 | 0.034045 | 0.014957 | 0.041671 | 0.030504 | 0.010285 | -0.002604 | -0.001699 | 0.002216 | -0.002006 | -0.007754 | 0.014799 | -0.013415 | -0.007162 | -0.011935 | -0.009310 | -0.011407 | -0.015701 | 0.001236 | 0.042659 | -0.009604 | -0.005194 | -0.008807 | -0.003277 | -0.008895 | -0.013547 | 0.007519 | 0.005049 | 0.005943 | 0.004948 | -0.019169 | 0.020231 | 0.016481 | 0.016893 |
| 2 | -0.009558 | -0.002915 | -0.007476 | -0.024771 | -0.020334 | 0.038391 | 0.016588 | 0.027680 | -0.012058 | -0.015430 | -0.088068 | 0.132410 | 0.001676 | -0.005603 | 0.199665 | -0.045203 | -0.017434 | 0.001603 | -0.026183 | -0.123932 | 0.120041 | -0.020735 | 0.032304 | -0.022004 | 0.054422 | -0.025230 | -0.050616 | 0.050011 | 0.025553 | 0.107484 | 0.009598 | 0.008606 | -0.005622 | -0.012435 | 0.005622 | 0.004551 | -0.006127 | -0.001365 | -0.001993 | 0.008558 | -0.001815 | -0.044310 | -0.003370 | -0.010889 | -0.022657 | -0.018948 | -0.014282 | -0.022490 | 0.024181 | 0.022081 | -0.023055 | 0.025485 | 0.003713 | 0.004413 | 0.042229 | -0.005784 | -0.033487 | 0.023155 | -0.004170 | -0.029246 | -0.029064 | 0.010672 | 0.013254 | -0.031620 | 0.002890 | -0.054504 | -0.030868 | 0.008395 | -0.036096 | 0.021120 | -0.076230 | 0.041613 | -0.053626 | -0.004247 | 0.000755 | -0.022376 | 0.001895 | -0.001251 | -0.009251 | 0.009011 | -0.020233 | -0.006532 | -0.013232 | 0.025025 | 0.012353 | 0.000933 | -0.007298 | 0.000265 | 0.006903 | 0.003649 | -0.010117 | 0.006129 | 0.008177 | 0.029566 | -0.004409 | -0.025072 | -0.012810 | -0.006106 | -0.000866 | 0.014103 | -0.019084 | -0.008888 | -0.005360 | 0.000190 | -0.003531 | 0.005602 | 0.005222 | -0.002071 | 0.057018 | 0.002671 | 0.058971 | 0.078923 | -0.007374 | -0.020383 | -0.007858 | -0.023716 | 0.046050 | 0.002959 | -0.016577 | 0.012717 | 0.024449 | -0.017599 | 0.020545 | -0.018387 | -0.013470 | 0.002256 | -0.055932 | -0.020237 | -0.031016 | 0.024260 | -0.033500 | 0.023093 | 0.028615 | 0.204211 | 0.200017 | -0.007939 | -0.015461 | 0.200265 | 0.067853 | 0.180875 | 0.000131 | 0.005400 | -0.086167 | -0.044815 | 0.003142 | -0.047620 | -0.095658 | -0.092261 | -0.091914 | -0.016939 | -0.054477 | -0.057370 | -0.078396 | 0.042849 | -0.023724 | 0.036179 | 0.008337 | 0.036179 | -0.007688 | 0.033159 | -0.023720 | -0.014008 | 0.002006 | -0.003261 | 0.074751 | 0.000732 | 0.023585 | 0.034583 | -0.017181 | -0.003819 | -0.009213 | 0.002232 | 0.000430 | -0.001835 | -0.002031 | -0.051639 | -0.040130 | 0.002836 | 0.000196 | 0.019113 | -0.030868 | 0.009010 | 0.047982 | -0.030868 | 0.014651 | 0.012661 | 0.017619 | 0.029973 | 0.014537 | -0.000919 | 0.002718 | 0.017070 | 0.009644 | -0.008559 | -0.013054 | 0.012086 | -0.009062 | -0.015039 | -0.008638 | 0.021936 | 0.027994 | 0.005938 | 0.014886 | 0.017344 | -0.008883 | -0.009309 | -0.017962 | 0.011700 | -0.003673 | -0.025419 | 0.010928 | 0.076176 | -0.067382 | 0.041946 | 0.003305 | 0.018661 | -0.018327 | -0.014620 | -0.009352 | -0.055505 | -0.020240 | -0.032774 | 0.033666 | -0.034374 | 0.014160 | 0.034544 | 0.204583 | 0.200295 | -0.008803 | -0.019059 | 0.200210 | 0.067511 | 0.183205 | 0.002864 | 0.006318 | -0.088525 | -0.049925 | 0.001311 | -0.048140 | -0.096386 | -0.091732 | -0.090870 | -0.020364 | -0.054677 | -0.056697 | -0.078981 | 0.046532 | -0.025240 | 0.034483 | 0.010177 | 0.034489 | -0.012289 | 0.032757 | -0.023414 | -0.019854 | 0.003808 | -0.017614 | 0.072803 | 0.001521 | 0.023646 | 0.034437 | -0.018016 | 0.004707 | -0.000628 | 0.006227 | 0.005122 | 0.003693 | 0.014880 | -0.041753 | -0.035407 | -0.004397 | 0.005767 | 0.018427 | -0.030868 | 0.012474 | 0.046881 | -0.030868 | 0.016300 | 0.014334 | 0.017109 | 0.029830 | 0.014043 | -0.003422 | 0.005585 | 0.020064 | 0.011027 | -0.009660 | -0.020276 | 0.011826 | -0.010247 | -0.015303 | -0.009587 | 0.021821 | 0.031732 | 0.025793 | 0.020591 | 0.008245 | 0.019367 | 0.012490 | -0.010057 | -0.009248 | -0.019912 | 0.011688 | -0.003652 | -0.026180 | 0.010916 | 0.077739 | -0.067507 | 0.047150 | 0.004028 | 0.019468 | -0.017796 | -0.012495 | 0.002881 | -0.046181 | -0.018969 | -0.034256 | 0.023709 | -0.036800 | -0.003922 | -0.001880 | 0.205511 | 0.198934 | -0.015776 | -0.015256 | 0.197745 | 0.068400 | 0.184115 | -0.001068 | 0.005432 | -0.095166 | -0.079410 | -0.023688 | -0.060693 | -0.088810 | -0.090387 | -0.088303 | -0.010001 | -0.050207 | -0.055659 | -0.083483 | 0.041970 | -0.026208 | 0.036313 | 0.009583 | 0.036054 | -0.007775 | 0.033053 | -0.023840 | -0.013449 | 0.003904 | -0.002649 | 0.074476 | 0.004681 | 0.025721 | 0.035023 | 0.001136 | -0.003797 | -0.015865 | 0.003313 | 0.001214 | -0.012171 | 0.019452 | -0.038584 | -0.039799 | -0.000611 | 0.053978 | 0.018935 | -0.030868 | 0.008132 | 0.080261 | 0.000059 | -0.024102 | 0.045490 | -0.014535 | -0.003633 | -0.028156 | 0.005935 | 0.013782 | 0.014337 | -0.009396 | -0.012577 | 0.011824 | -0.008640 | -0.002282 | -0.008180 | -0.007000 | 0.004290 | 0.017374 | 0.002688 | 0.014227 | -0.008849 | -0.008834 | -0.017204 | 0.011726 | -0.003598 | -0.025174 | 0.010824 | 0.077021 | -0.069264 | 0.041587 | 0.006158 | -0.063451 | -0.050322 | 0.032628 | -0.043922 | 0.034997 | 0.031385 | 0.046276 | 0.045743 | 0.008897 | 0.014522 | 0.045180 | 0.008148 | 0.016006 | 0.051939 | 0.007912 | 0.012502 | -0.007540 | -0.004872 | -0.007885 | -0.003807 | -0.000681 | 0.007513 | -0.021643 | -0.016510 | -0.020436 | -0.018005 | -0.020177 | -0.024021 | -0.006030 | 0.025972 | -0.012056 | -0.015590 | -0.010646 | -0.011263 | -0.010915 | -0.020869 | 0.001154 | 0.000928 | 0.002010 | 0.000876 | -0.020468 | 0.003780 | 0.004044 | -0.008272 |
| 3 | 0.010854 | 0.007781 | 0.013249 | 0.035184 | 0.033649 | -0.045515 | -0.025413 | -0.028075 | 0.002808 | -0.011834 | -0.006563 | 0.094788 | 0.001203 | -0.000029 | 0.173727 | 0.024465 | -0.039179 | -0.005139 | 0.034373 | 0.116134 | -0.117136 | 0.012628 | -0.032754 | 0.036877 | -0.040035 | 0.038448 | 0.083745 | -0.064128 | -0.037864 | -0.110609 | -0.019324 | -0.021109 | -0.000375 | 0.007259 | 0.000375 | -0.020043 | -0.017718 | -0.005282 | 0.016609 | -0.016466 | 0.026067 | 0.016032 | 0.039047 | 0.050926 | -0.014314 | -0.026556 | 0.040350 | -0.003834 | -0.031301 | -0.025113 | 0.035064 | -0.065596 | -0.015259 | -0.044900 | -0.066120 | 0.033482 | 0.009849 | 0.004931 | -0.031436 | 0.021444 | 0.020899 | 0.036189 | -0.029610 | 0.027406 | 0.038220 | 0.064505 | 0.036272 | -0.003951 | 0.043546 | -0.023097 | 0.098710 | -0.062516 | 0.052779 | -0.003787 | 0.002104 | 0.044419 | 0.006054 | 0.013063 | 0.019507 | 0.007331 | 0.014766 | 0.010903 | 0.017954 | -0.033122 | 0.011081 | -0.000420 | -0.001781 | 0.001542 | -0.009572 | 0.003116 | 0.010897 | -0.009472 | -0.019926 | -0.048794 | -0.002960 | 0.033173 | -0.009728 | 0.013818 | 0.016129 | -0.013389 | 0.018979 | 0.001414 | 0.015118 | 0.011599 | -0.000111 | -0.014956 | 0.009390 | -0.014464 | -0.060227 | 0.013643 | -0.054944 | -0.083833 | 0.033245 | 0.005441 | 0.016848 | 0.006645 | -0.049735 | -0.013318 | -0.003531 | -0.054887 | -0.042454 | 0.011208 | -0.008204 | 0.003607 | 0.000817 | -0.023384 | 0.083413 | 0.033389 | 0.049770 | -0.019368 | 0.044198 | -0.012186 | -0.024894 | 0.160601 | 0.184892 | -0.001402 | 0.014174 | 0.179971 | 0.009858 | 0.184470 | 0.007304 | -0.008434 | 0.071706 | 0.028282 | -0.015430 | 0.053384 | 0.079414 | 0.075173 | 0.075520 | 0.024663 | 0.066628 | 0.062069 | 0.095334 | -0.063457 | 0.038109 | -0.043265 | -0.009743 | -0.043270 | 0.000153 | -0.037728 | 0.003697 | 0.067419 | 0.045620 | 0.037192 | -0.069895 | 0.016081 | 0.004949 | -0.011861 | -0.008069 | -0.006559 | 0.001123 | -0.007192 | 0.000242 | -0.007362 | -0.009827 | 0.062845 | 0.044032 | -0.009231 | -0.012484 | -0.034382 | 0.036272 | -0.019765 | -0.047196 | 0.036272 | -0.024510 | -0.016891 | -0.012335 | -0.027915 | -0.014889 | 0.010290 | -0.012320 | 0.017622 | -0.036481 | 0.016295 | 0.000698 | -0.014456 | 0.002458 | 0.026616 | 0.000043 | -0.041352 | -0.044654 | -0.012724 | -0.007687 | -0.032496 | 0.000641 | 0.001542 | 0.016239 | -0.021615 | -0.002495 | 0.023868 | -0.025677 | -0.098192 | 0.059039 | -0.019516 | -0.017371 | -0.016933 | 0.001683 | 0.000882 | -0.009463 | 0.084055 | 0.033390 | 0.051358 | -0.031360 | 0.044466 | -0.002261 | -0.035147 | 0.159707 | 0.184654 | 0.000029 | 0.018774 | 0.180054 | 0.007271 | 0.183165 | 0.002531 | -0.007833 | 0.072751 | 0.033295 | -0.013882 | 0.053549 | 0.079734 | 0.074727 | 0.074639 | 0.028702 | 0.067332 | 0.062334 | 0.094758 | -0.062106 | 0.039609 | -0.040165 | -0.012729 | -0.040174 | 0.012396 | -0.040401 | 0.003182 | 0.070129 | 0.045467 | 0.049475 | -0.073076 | 0.013831 | 0.004544 | -0.013800 | -0.007155 | -0.018595 | -0.009850 | -0.012093 | -0.006747 | -0.015374 | -0.019673 | 0.048314 | 0.038090 | -0.000782 | -0.019789 | -0.033137 | 0.036272 | -0.024440 | -0.047863 | 0.036272 | -0.023626 | -0.016500 | -0.010701 | -0.028368 | -0.016630 | 0.010293 | -0.017704 | 0.019261 | -0.035704 | 0.018019 | 0.007484 | -0.014320 | 0.003248 | 0.026760 | 0.003241 | -0.043529 | -0.050523 | -0.041448 | -0.037049 | -0.009469 | -0.014773 | -0.023731 | 0.001626 | 0.001514 | 0.019276 | -0.021524 | -0.002503 | 0.027545 | -0.026236 | -0.102456 | 0.057880 | -0.023196 | -0.017069 | -0.007394 | 0.002736 | -0.000634 | -0.024576 | 0.065372 | 0.034861 | 0.053554 | -0.019966 | 0.047750 | 0.014590 | 0.001316 | 0.162448 | 0.185827 | 0.001648 | 0.014825 | 0.169785 | 0.008774 | 0.186160 | 0.008533 | -0.008825 | 0.077725 | 0.060070 | 0.014241 | 0.062173 | 0.070360 | 0.072497 | 0.070113 | 0.013297 | 0.062288 | 0.063617 | 0.100384 | -0.061701 | 0.043454 | -0.043193 | -0.010109 | -0.043565 | 0.000714 | -0.037290 | 0.003805 | 0.064689 | 0.044500 | 0.031625 | -0.071941 | 0.020057 | 0.005963 | -0.012894 | -0.007979 | -0.005992 | 0.027360 | -0.009827 | 0.000718 | 0.005765 | 0.005000 | 0.051324 | 0.048061 | -0.005933 | -0.057830 | -0.034743 | 0.036272 | -0.019113 | -0.077520 | -0.012318 | 0.025657 | -0.039675 | 0.018465 | -0.013041 | 0.037917 | -0.012445 | -0.001540 | -0.041922 | 0.017120 | -0.000325 | -0.014130 | 0.001601 | 0.024810 | -0.001114 | -0.002162 | 0.004078 | -0.036869 | -0.011952 | -0.029064 | 0.001146 | 0.005329 | 0.013583 | -0.021606 | -0.003487 | 0.023822 | -0.025704 | -0.098151 | 0.063062 | -0.018850 | -0.018293 | 0.078162 | 0.070664 | -0.041165 | 0.063385 | -0.045463 | -0.038890 | -0.056101 | -0.059040 | -0.018288 | -0.019877 | -0.059898 | -0.020369 | -0.021589 | -0.062917 | -0.016755 | -0.017148 | 0.003814 | -0.001068 | 0.001867 | -0.001570 | 0.034514 | -0.004723 | 0.030420 | 0.018507 | 0.029125 | 0.020814 | 0.026227 | 0.025173 | 0.000985 | -0.053263 | 0.022451 | 0.023832 | 0.021272 | 0.019793 | 0.021248 | 0.035123 | -0.007604 | 0.001543 | 0.000760 | 0.001669 | 0.006951 | 0.008462 | 0.008082 | 0.000881 |
| 4 | -0.007996 | 0.001429 | -0.001382 | -0.016116 | 0.003228 | 0.020745 | -0.007479 | 0.013996 | -0.010908 | 0.004901 | 0.002307 | -0.021070 | 0.028138 | -0.018952 | -0.035464 | -0.024547 | 0.005832 | 0.002343 | -0.030160 | -0.003642 | -0.011767 | 0.003729 | 0.012095 | -0.027122 | -0.026019 | -0.028869 | -0.006835 | -0.004508 | -0.011049 | 0.021030 | 0.009315 | -0.000076 | 0.008284 | 0.004084 | -0.008284 | 0.018634 | -0.023468 | 0.019541 | -0.007080 | 0.000135 | 0.099599 | -0.073522 | 0.106893 | 0.141346 | -0.075913 | -0.076897 | 0.128653 | -0.075458 | 0.013725 | 0.014585 | 0.048854 | -0.103113 | -0.003928 | -0.095605 | 0.009982 | 0.112976 | -0.083768 | 0.126449 | -0.016342 | 0.005492 | -0.008528 | 0.181239 | 0.025985 | -0.052492 | 0.161158 | 0.014950 | -0.116287 | -0.009789 | -0.011791 | -0.029931 | -0.045670 | 0.008734 | 0.002126 | -0.022455 | -0.052816 | 0.003972 | 0.001410 | -0.002745 | 0.010071 | -0.011512 | -0.001074 | 0.007707 | 0.001119 | 0.017005 | 0.016211 | 0.004792 | -0.005852 | -0.005129 | 0.003927 | 0.017244 | 0.002248 | 0.004635 | 0.000196 | -0.017229 | -0.015904 | -0.017515 | -0.013970 | -0.008015 | -0.016576 | -0.001793 | -0.013150 | 0.021902 | 0.004694 | -0.007352 | -0.015305 | -0.004296 | -0.015622 | 0.005883 | 0.018660 | 0.001289 | 0.019687 | 0.033095 | -0.001728 | -0.029735 | -0.006234 | -0.020162 | 0.024550 | -0.025191 | -0.008861 | -0.073601 | -0.013960 | 0.017383 | -0.001736 | -0.010559 | -0.017016 | -0.020602 | -0.016153 | 0.003177 | -0.004021 | -0.026712 | 0.047678 | 0.003416 | 0.012730 | -0.029296 | -0.036223 | -0.028611 | -0.073704 | -0.035409 | 0.014576 | -0.034744 | -0.003583 | 0.000060 | 0.012505 | 0.011562 | -0.002177 | -0.011413 | 0.017698 | 0.014645 | 0.019111 | -0.005533 | -0.016814 | -0.015427 | -0.009719 | -0.052412 | 0.030329 | -0.020237 | -0.006135 | -0.020249 | -0.095645 | -0.028265 | 0.021247 | 0.107383 | 0.184368 | 0.146527 | 0.029159 | 0.160658 | 0.180456 | 0.153574 | -0.029209 | -0.049580 | 0.010002 | 0.034865 | 0.065953 | -0.002913 | -0.055101 | -0.023799 | -0.070858 | -0.067130 | -0.023758 | 0.015752 | -0.116287 | -0.007167 | 0.019336 | -0.116287 | 0.002559 | -0.010832 | -0.008850 | 0.006130 | 0.012050 | 0.028264 | -0.012389 | 0.023253 | -0.003261 | 0.016437 | 0.001636 | 0.002883 | -0.003629 | 0.009484 | 0.025919 | 0.013116 | 0.013823 | 0.001052 | -0.024662 | -0.008941 | 0.033189 | 0.022042 | 0.034297 | 0.022852 | -0.015604 | 0.010392 | 0.014396 | 0.014403 | -0.057719 | 0.064038 | -0.016191 | -0.002753 | -0.011171 | -0.014586 | -0.017318 | -0.013483 | 0.003175 | -0.003483 | -0.021225 | 0.050394 | 0.002640 | 0.013308 | -0.029208 | -0.035780 | -0.030033 | -0.085922 | -0.035422 | 0.014549 | -0.033274 | -0.005879 | -0.000420 | 0.012554 | 0.011216 | -0.002132 | -0.013347 | 0.015266 | 0.013612 | 0.017570 | -0.006530 | -0.018541 | -0.016548 | -0.010437 | -0.049323 | 0.027077 | -0.021149 | -0.011368 | -0.021163 | -0.093248 | -0.026082 | 0.022001 | 0.120929 | 0.177885 | 0.141236 | 0.008638 | 0.159755 | 0.181648 | 0.146586 | -0.031922 | -0.063899 | 0.026895 | 0.041608 | 0.069735 | 0.016911 | 0.068823 | -0.023266 | -0.079369 | -0.084702 | -0.004450 | 0.018064 | -0.116287 | 0.005935 | 0.022376 | -0.116287 | 0.010970 | -0.012583 | -0.006417 | 0.005915 | 0.013402 | 0.032882 | -0.017062 | 0.025469 | -0.004561 | 0.015103 | 0.001531 | 0.000808 | -0.003320 | 0.009642 | 0.025356 | 0.011330 | 0.015777 | 0.018984 | 0.007346 | 0.001049 | -0.027366 | -0.002194 | 0.033120 | 0.022094 | 0.035390 | 0.022932 | -0.015608 | 0.011526 | 0.013307 | 0.012194 | -0.057687 | 0.065323 | -0.014894 | -0.001836 | -0.010361 | -0.017034 | -0.020841 | -0.014382 | -0.010046 | -0.003916 | -0.026791 | 0.047052 | 0.002306 | -0.014111 | -0.029703 | -0.036280 | -0.067433 | -0.070548 | -0.032953 | 0.015242 | -0.035103 | -0.002587 | 0.000759 | 0.015226 | 0.015507 | 0.008767 | -0.000153 | 0.017720 | 0.016420 | 0.016354 | -0.005085 | -0.021089 | -0.010562 | -0.011033 | -0.053904 | 0.030915 | -0.020422 | -0.006253 | -0.019951 | -0.096021 | -0.027917 | 0.020846 | 0.098310 | 0.183865 | 0.134438 | 0.021294 | 0.179387 | 0.184547 | 0.149802 | 0.060759 | -0.048204 | 0.018432 | 0.032177 | 0.070719 | -0.032924 | 0.003005 | 0.001012 | -0.000532 | -0.077629 | 0.032370 | 0.020717 | -0.116287 | -0.014048 | 0.013093 | 0.011679 | -0.016360 | 0.023245 | -0.020808 | -0.012570 | 0.025954 | 0.000929 | 0.004524 | -0.004205 | 0.016665 | 0.001435 | 0.001754 | -0.002682 | 0.010387 | 0.024113 | -0.005802 | 0.000634 | -0.011962 | 0.013780 | -0.004463 | 0.033333 | 0.024542 | 0.032342 | 0.022834 | -0.015289 | 0.010208 | 0.014775 | 0.014368 | -0.054296 | 0.064169 | -0.019422 | -0.002424 | -0.000701 | -0.004447 | -0.000382 | 0.009166 | 0.000824 | -0.028412 | 0.003226 | 0.000408 | 0.005700 | 0.002531 | 0.000413 | 0.003471 | -0.004116 | 0.000525 | 0.007061 | 0.004730 | -0.013719 | -0.009214 | -0.015415 | 0.038920 | 0.032302 | 0.019074 | 0.004468 | 0.018686 | 0.005576 | 0.016163 | -0.002784 | 0.003295 | -0.029935 | 0.029441 | 0.019190 | 0.028784 | 0.018294 | 0.028176 | 0.025250 | -0.017115 | 0.014590 | 0.015684 | 0.014588 | -0.012769 | 0.030045 | 0.025936 | 0.015242 |
| 5 | 0.005652 | -0.000794 | -0.003400 | 0.020104 | -0.010305 | -0.029797 | -0.004642 | 0.008338 | 0.002992 | -0.011679 | -0.020824 | 0.002922 | -0.036815 | 0.004896 | 0.003035 | -0.008329 | 0.000846 | 0.008170 | 0.020730 | 0.021627 | 0.002483 | -0.006748 | 0.004427 | -0.011944 | -0.014586 | -0.006580 | 0.004691 | -0.032462 | -0.027016 | -0.003106 | -0.005689 | -0.014135 | 0.006852 | 0.010502 | -0.006852 | 0.006210 | 0.017168 | -0.009124 | 0.001696 | 0.013617 | -0.139871 | 0.050014 | -0.080886 | -0.157837 | 0.021099 | 0.034656 | -0.156202 | -0.072589 | -0.008536 | -0.000875 | -0.125891 | 0.122807 | -0.020112 | 0.105622 | -0.013300 | -0.149731 | 0.055201 | -0.089709 | -0.015997 | 0.012957 | 0.039711 | -0.168736 | -0.032830 | 0.042910 | -0.155916 | 0.000025 | 0.072589 | 0.033021 | 0.043917 | 0.032488 | 0.034688 | 0.009607 | -0.025588 | 0.020749 | 0.012069 | -0.016043 | -0.013678 | -0.004007 | -0.018470 | 0.020451 | 0.008386 | 0.013847 | 0.002398 | 0.000591 | -0.013487 | -0.027264 | -0.009483 | 0.031762 | -0.025824 | -0.007414 | -0.013047 | -0.025175 | -0.006348 | 0.003314 | 0.008862 | 0.001342 | 0.015305 | 0.025018 | 0.003889 | 0.008698 | -0.022080 | 0.018555 | 0.001702 | -0.008372 | 0.001132 | 0.013632 | 0.006609 | -0.005718 | -0.021777 | 0.078886 | -0.020254 | -0.023016 | 0.082112 | -0.030724 | 0.075753 | -0.024238 | -0.066033 | -0.059423 | 0.000397 | 0.013588 | 0.029041 | -0.004623 | -0.014643 | 0.005021 | -0.000650 | 0.009745 | -0.008069 | -0.010310 | -0.015418 | -0.032608 | -0.028731 | -0.006277 | -0.015019 | 0.006448 | 0.001800 | -0.029562 | 0.043669 | 0.003326 | 0.006955 | -0.003021 | -0.010196 | -0.010005 | 0.018997 | 0.026967 | 0.011505 | 0.019958 | 0.039035 | 0.028480 | 0.024861 | -0.002659 | 0.002963 | 0.005550 | -0.007186 | 0.049168 | -0.021301 | 0.007830 | 0.005967 | 0.007830 | 0.061550 | 0.030397 | -0.033429 | 0.008346 | 0.094237 | -0.013086 | 0.152464 | 0.190105 | 0.211297 | 0.226718 | 0.014323 | 0.040369 | -0.007127 | -0.038385 | -0.058114 | 0.003949 | 0.039808 | 0.004301 | 0.026414 | 0.061657 | -0.000281 | -0.027592 | 0.072589 | 0.005303 | -0.013966 | 0.072589 | 0.026966 | -0.012141 | -0.007713 | -0.025417 | 0.006421 | -0.008544 | 0.027011 | -0.056878 | 0.015567 | -0.027976 | -0.006788 | 0.005293 | 0.012233 | -0.008910 | 0.019697 | -0.004855 | -0.002524 | -0.016854 | 0.038817 | 0.006362 | 0.009514 | 0.016899 | 0.001399 | -0.023103 | 0.003240 | -0.014314 | 0.002135 | 0.007603 | 0.048824 | -0.090285 | 0.006296 | -0.012483 | 0.005782 | -0.002349 | -0.003601 | -0.006700 | -0.010308 | -0.017132 | -0.036493 | -0.027288 | -0.007284 | -0.016410 | 0.006963 | 0.002257 | -0.028137 | 0.050899 | 0.003319 | 0.008414 | -0.001218 | -0.007876 | -0.009467 | 0.020236 | 0.027428 | 0.011841 | 0.018207 | 0.037131 | 0.028194 | 0.025164 | -0.001077 | 0.002759 | 0.005862 | -0.004844 | 0.049558 | -0.021577 | 0.008016 | 0.009627 | 0.008018 | 0.059484 | 0.025902 | -0.033681 | -0.020247 | 0.080324 | -0.018131 | 0.156715 | 0.193979 | 0.211182 | 0.230019 | 0.012344 | 0.067336 | -0.020973 | -0.040586 | -0.064634 | -0.011874 | 0.040514 | 0.000126 | 0.034948 | 0.072676 | -0.013400 | -0.029123 | 0.072589 | -0.002458 | -0.011839 | 0.072589 | 0.026922 | -0.011342 | -0.009418 | -0.025706 | 0.011073 | -0.008517 | 0.030676 | -0.061340 | 0.015164 | -0.030060 | -0.009278 | 0.006597 | 0.012502 | -0.009061 | 0.020115 | -0.008672 | -0.007309 | -0.007594 | 0.002318 | -0.018304 | 0.039545 | 0.003286 | 0.010851 | 0.016930 | 0.001593 | -0.023452 | 0.003223 | -0.012496 | 0.000855 | 0.010179 | 0.046438 | -0.087858 | 0.006432 | -0.014655 | 0.004685 | -0.000404 | 0.010051 | -0.016153 | -0.008660 | -0.013353 | -0.030687 | -0.030025 | -0.006662 | 0.009700 | 0.006407 | 0.001286 | -0.005064 | 0.042704 | 0.004950 | 0.006930 | -0.002451 | -0.011272 | -0.009975 | 0.027191 | 0.032442 | 0.009483 | -0.005056 | 0.033648 | 0.030079 | 0.030038 | -0.003469 | 0.014648 | 0.018579 | -0.006757 | 0.050001 | -0.018869 | 0.007414 | 0.004789 | 0.008486 | 0.061136 | 0.030073 | -0.033601 | 0.023755 | 0.090261 | -0.000987 | 0.161915 | 0.187001 | 0.209340 | 0.230612 | 0.128304 | 0.038928 | -0.015948 | -0.034165 | -0.060913 | 0.030588 | 0.037802 | -0.018374 | -0.032674 | 0.068907 | -0.039274 | -0.030626 | 0.072589 | 0.011646 | -0.021742 | -0.002522 | 0.017273 | 0.017570 | -0.012538 | 0.011337 | 0.013742 | 0.010670 | -0.006341 | 0.016608 | -0.026920 | -0.006365 | 0.005949 | 0.011070 | -0.018055 | 0.017367 | -0.003549 | 0.001697 | -0.004973 | -0.014353 | 0.005739 | 0.008824 | 0.004943 | 0.002122 | -0.023357 | 0.003568 | -0.014049 | 0.001469 | 0.007822 | 0.048513 | -0.090671 | 0.007757 | 0.024352 | 0.002415 | 0.004233 | -0.001788 | -0.021870 | 0.002186 | 0.002896 | -0.019774 | -0.031353 | -0.033076 | -0.017763 | -0.033250 | -0.031656 | -0.015279 | -0.031510 | -0.033272 | -0.018158 | -0.010296 | -0.014925 | -0.007706 | -0.012424 | 0.003648 | -0.024157 | -0.007534 | -0.024503 | -0.007658 | -0.023039 | -0.005661 | -0.012033 | 0.027949 | 0.003391 | 0.009678 | 0.003375 | 0.009501 | 0.004391 | 0.004167 | 0.020458 | -0.011780 | -0.009878 | -0.011744 | -0.003763 | -0.021807 | -0.017902 | -0.001947 |
| 6 | -0.004720 | 0.006791 | 0.014286 | -0.017307 | 0.001912 | 0.005853 | -0.011250 | 0.002280 | 0.008645 | -0.011470 | 0.006550 | -0.013059 | 0.013789 | 0.040814 | -0.016960 | -0.011926 | -0.000654 | -0.006087 | -0.030852 | -0.027174 | 0.005759 | 0.001440 | 0.032585 | -0.055787 | -0.032763 | -0.058848 | -0.020332 | 0.035200 | 0.025513 | 0.052634 | -0.094322 | -0.062169 | -0.134405 | -0.129174 | 0.134405 | -0.030600 | -0.025054 | -0.131620 | 0.028154 | 0.004288 | 0.067819 | -0.063159 | 0.069288 | 0.093988 | -0.046890 | -0.064230 | 0.078204 | -0.020665 | 0.017354 | 0.021966 | 0.032632 | -0.058976 | -0.014148 | -0.069812 | -0.075611 | 0.097385 | -0.067516 | -0.000482 | -0.086505 | -0.003141 | 0.032865 | 0.059329 | -0.098478 | 0.005179 | 0.065148 | 0.010698 | 0.157383 | 0.001902 | 0.005608 | -0.014060 | -0.036078 | -0.014997 | 0.008829 | -0.003114 | -0.014063 | -0.003556 | 0.004145 | 0.011334 | -0.008846 | -0.018739 | 0.001289 | -0.002566 | -0.002810 | 0.006005 | 0.018856 | 0.022879 | 0.012409 | -0.026105 | 0.032865 | 0.011521 | -0.011780 | 0.035054 | -0.002113 | -0.010503 | -0.010983 | -0.006409 | -0.018060 | -0.012672 | 0.008097 | -0.007645 | -0.018318 | 0.022554 | -0.005423 | 0.035621 | -0.011653 | -0.007972 | 0.027998 | -0.019764 | 0.035531 | -0.042369 | 0.024883 | 0.039254 | -0.037005 | -0.008039 | -0.047613 | -0.001267 | 0.025698 | 0.028494 | 0.024682 | -0.030479 | 0.001753 | -0.002378 | -0.001247 | -0.007799 | 0.007590 | 0.002659 | 0.002712 | 0.001887 | 0.008082 | -0.028934 | 0.031154 | 0.030817 | 0.020432 | -0.015641 | -0.016367 | -0.001192 | 0.091208 | -0.018177 | 0.006599 | -0.010804 | 0.012393 | 0.016793 | -0.003419 | 0.001473 | 0.003260 | -0.047589 | 0.005907 | 0.012206 | 0.018498 | -0.007762 | -0.060263 | -0.059378 | -0.036211 | 0.023636 | 0.036510 | 0.203307 | 0.158310 | 0.203302 | 0.000231 | 0.019046 | -0.041305 | 0.043376 | 0.039574 | 0.052750 | -0.010383 | 0.006922 | 0.001354 | 0.004187 | -0.020347 | -0.022727 | -0.028323 | -0.052287 | -0.020442 | -0.030839 | 0.028462 | 0.102023 | 0.102960 | 0.044666 | -0.024514 | -0.068237 | 0.157383 | -0.026322 | -0.023687 | 0.157383 | 0.007235 | 0.010005 | 0.000046 | 0.026374 | 0.027394 | 0.005603 | 0.001762 | 0.039799 | -0.010893 | 0.016090 | -0.032000 | 0.019441 | 0.007607 | 0.005716 | -0.005885 | 0.023858 | 0.023582 | -0.015954 | -0.028991 | -0.034744 | 0.025081 | 0.022180 | -0.003170 | -0.062239 | -0.014010 | 0.012895 | -0.025138 | -0.010599 | -0.032925 | 0.048574 | 0.005764 | -0.005248 | -0.005529 | 0.007458 | 0.007738 | 0.002718 | 0.001887 | 0.007539 | -0.023645 | 0.028124 | 0.029505 | 0.024280 | -0.015910 | -0.017025 | -0.003015 | 0.107564 | -0.018149 | 0.006311 | -0.012866 | 0.009296 | 0.017310 | -0.001382 | 0.000556 | 0.004435 | -0.046566 | 0.007159 | 0.012502 | 0.018026 | -0.004342 | -0.059117 | -0.055271 | -0.040140 | 0.019576 | 0.037797 | 0.200902 | 0.161355 | 0.200907 | 0.000732 | 0.010526 | -0.044349 | 0.062636 | 0.044665 | 0.050294 | -0.021712 | 0.004747 | 0.001792 | 0.000853 | -0.018995 | -0.030670 | -0.060696 | -0.066506 | -0.030235 | -0.062543 | -0.065725 | 0.073981 | 0.105186 | 0.066483 | -0.054509 | -0.074108 | 0.157383 | -0.050063 | -0.026239 | 0.157383 | 0.007184 | 0.013072 | 0.000356 | 0.025573 | 0.025354 | 0.007393 | 0.001488 | 0.044592 | -0.010669 | 0.012565 | -0.031613 | 0.017813 | 0.007199 | 0.006891 | -0.007313 | 0.026643 | 0.026212 | 0.033517 | 0.029227 | -0.015060 | -0.027032 | -0.020094 | 0.026005 | 0.022237 | -0.002601 | -0.062334 | -0.013923 | 0.012258 | -0.025381 | -0.010664 | -0.033359 | 0.046927 | 0.007492 | -0.001390 | -0.007794 | 0.007136 | 0.001809 | 0.006991 | -0.005333 | 0.008336 | -0.028537 | 0.031087 | 0.012004 | 0.012511 | -0.015622 | -0.015595 | -0.017781 | 0.088708 | -0.017458 | 0.007742 | -0.011576 | 0.011995 | 0.016393 | 0.006519 | 0.011152 | 0.006612 | -0.030835 | 0.016007 | 0.015714 | 0.017466 | -0.005898 | -0.048899 | -0.065215 | -0.037941 | 0.032418 | 0.047830 | 0.206998 | 0.168510 | 0.196724 | 0.001246 | 0.019391 | -0.038234 | 0.036718 | 0.041253 | 0.044113 | -0.015804 | 0.018217 | 0.004649 | 0.002344 | -0.007518 | -0.021563 | 0.008140 | -0.059455 | -0.015260 | 0.014402 | 0.064008 | 0.078910 | 0.033775 | 0.054736 | -0.047645 | -0.078059 | 0.157383 | -0.020668 | -0.020248 | -0.010255 | -0.000577 | 0.009944 | 0.032827 | -0.000526 | -0.002990 | 0.001828 | 0.005380 | -0.007758 | 0.015224 | -0.032442 | 0.018509 | 0.008372 | 0.003062 | -0.005863 | 0.009295 | 0.005689 | -0.025697 | 0.004189 | -0.028309 | 0.025158 | 0.023973 | -0.002443 | -0.062068 | -0.018023 | 0.012713 | -0.025294 | -0.009411 | -0.031814 | 0.048382 | 0.006400 | -0.011213 | 0.024108 | 0.019189 | 0.027089 | 0.004792 | 0.003829 | -0.004602 | 0.008407 | -0.030081 | -0.014447 | 0.007329 | -0.028531 | -0.014961 | 0.007699 | -0.029969 | -0.013890 | 0.014277 | -0.004378 | -0.003048 | -0.006571 | 0.020016 | -0.007582 | 0.008110 | -0.009063 | 0.009640 | -0.008134 | 0.006401 | -0.008825 | 0.022663 | -0.014396 | -0.055014 | -0.068241 | -0.052786 | -0.061548 | -0.055824 | -0.063943 | 0.004497 | -0.023124 | -0.022737 | -0.023211 | -0.009287 | 0.029254 | 0.029848 | 0.026512 |
| 7 | -0.018904 | -0.016001 | 0.018385 | -0.024806 | -0.012114 | 0.021103 | -0.001451 | -0.012439 | -0.014098 | 0.008479 | -0.021603 | -0.009775 | 0.011948 | 0.042838 | -0.007288 | -0.030153 | -0.005204 | 0.008788 | -0.015723 | -0.059115 | 0.039650 | -0.023359 | -0.000789 | -0.027159 | 0.005614 | -0.027576 | -0.032063 | 0.046450 | 0.023064 | 0.061886 | 0.091884 | 0.050417 | 0.110471 | 0.099896 | -0.110471 | 0.015473 | 0.025593 | 0.094291 | -0.014888 | 0.003576 | 0.079627 | -0.011382 | 0.002194 | 0.046948 | -0.018453 | -0.002299 | 0.058229 | -0.017185 | 0.001599 | -0.006471 | 0.073143 | -0.047509 | 0.022245 | -0.011791 | -0.028365 | 0.092201 | 0.023111 | -0.077886 | -0.044834 | 0.023447 | 0.058863 | -0.004430 | -0.085657 | 0.022287 | -0.000697 | -0.000955 | 0.144059 | -0.019491 | -0.010415 | -0.001506 | -0.057062 | 0.058069 | -0.022043 | -0.001351 | -0.041480 | -0.026613 | -0.007819 | 0.000635 | 0.000545 | 0.015572 | -0.030966 | -0.005407 | -0.009813 | 0.005991 | 0.010040 | -0.011803 | 0.030184 | 0.006477 | 0.002437 | -0.014603 | -0.017034 | 0.001634 | 0.016220 | 0.038011 | 0.014111 | -0.006195 | -0.010785 | 0.004031 | 0.023510 | 0.003605 | -0.004608 | 0.021705 | -0.010101 | 0.060582 | -0.029544 | -0.006767 | -0.035124 | 0.020738 | -0.021447 | -0.033742 | -0.019859 | -0.003948 | -0.029142 | 0.009385 | -0.022334 | 0.039829 | 0.070052 | 0.014716 | 0.001783 | 0.031717 | -0.007068 | -0.000399 | -0.012963 | -0.011855 | -0.020915 | 0.014941 | -0.017235 | -0.012151 | -0.032127 | 0.002140 | -0.011452 | 0.015816 | 0.024246 | 0.006839 | -0.011259 | 0.007822 | 0.086565 | -0.009671 | 0.040645 | -0.014921 | -0.010377 | -0.007185 | -0.037293 | -0.021899 | -0.002002 | -0.053959 | -0.039237 | -0.041499 | -0.040437 | -0.048211 | -0.052056 | -0.048237 | -0.049269 | -0.022592 | -0.016751 | -0.199387 | -0.162078 | -0.199382 | -0.060858 | -0.011119 | 0.046570 | 0.032763 | 0.045368 | 0.044198 | -0.060243 | 0.029296 | 0.012290 | -0.016948 | 0.006331 | -0.035798 | -0.027920 | -0.057613 | -0.037179 | -0.041220 | 0.027995 | 0.114959 | 0.103102 | 0.019568 | -0.015884 | -0.061655 | 0.144059 | -0.047499 | -0.010148 | 0.144059 | 0.039945 | 0.009896 | 0.002389 | 0.026501 | 0.033107 | 0.014267 | -0.005064 | 0.028506 | 0.025510 | 0.020693 | -0.020998 | 0.005232 | 0.005345 | -0.024677 | 0.047060 | 0.007078 | -0.003511 | -0.006789 | -0.027564 | 0.054536 | 0.033776 | 0.022683 | 0.054986 | -0.095991 | -0.035671 | -0.004379 | 0.033850 | 0.026409 | 0.011477 | -0.031860 | -0.023446 | -0.016248 | -0.010960 | -0.015308 | 0.016994 | -0.021631 | -0.012154 | -0.030585 | 0.006287 | -0.010109 | 0.011280 | 0.029369 | 0.007477 | -0.010970 | 0.011565 | 0.100895 | -0.009696 | 0.042090 | -0.013381 | -0.007094 | -0.006613 | -0.039225 | -0.023542 | -0.001863 | -0.053672 | -0.040188 | -0.041315 | -0.040355 | -0.047526 | -0.050758 | -0.050885 | -0.047677 | -0.016867 | -0.020334 | -0.200812 | -0.162310 | -0.200813 | -0.058692 | -0.004312 | 0.047197 | 0.037366 | 0.048453 | 0.042304 | -0.065841 | 0.029093 | 0.012541 | -0.022010 | 0.005294 | -0.050388 | -0.066895 | -0.070479 | -0.050821 | -0.076494 | -0.074352 | 0.096033 | 0.102390 | 0.040665 | -0.041676 | -0.065060 | 0.144059 | -0.073030 | -0.012813 | 0.144059 | 0.045253 | 0.012386 | 0.003025 | 0.027051 | 0.036791 | 0.015616 | -0.006545 | 0.029653 | 0.024240 | 0.021577 | -0.020263 | 0.004292 | 0.005952 | -0.023569 | 0.047620 | 0.006856 | -0.002549 | 0.000614 | 0.009212 | -0.004948 | -0.027408 | 0.041403 | 0.033598 | 0.022665 | 0.055684 | -0.095964 | -0.035573 | -0.006222 | 0.033995 | 0.028098 | 0.010649 | -0.035309 | -0.020975 | -0.013113 | -0.010718 | -0.019235 | 0.014155 | -0.008690 | -0.009241 | -0.032608 | 0.002750 | -0.009828 | -0.008461 | 0.004694 | 0.005901 | -0.011652 | 0.003844 | 0.085764 | -0.006412 | 0.040460 | -0.014431 | -0.010799 | -0.006176 | -0.040045 | -0.034998 | -0.018020 | -0.028151 | -0.035619 | -0.037683 | -0.036706 | -0.042855 | -0.051374 | -0.046798 | -0.051976 | -0.031696 | -0.025540 | -0.201936 | -0.168221 | -0.194530 | -0.061187 | -0.011541 | 0.044334 | 0.025035 | 0.045534 | 0.044834 | -0.062261 | 0.034892 | 0.012871 | -0.018710 | 0.023376 | -0.035973 | -0.024218 | -0.065018 | -0.037490 | 0.007658 | -0.014470 | 0.082745 | 0.028035 | 0.032573 | -0.050353 | -0.070465 | 0.144059 | -0.040123 | -0.005613 | -0.016374 | -0.007614 | 0.010657 | -0.001277 | -0.020225 | -0.022020 | -0.021629 | -0.018588 | 0.027054 | 0.021573 | -0.020659 | 0.004543 | 0.004800 | -0.013452 | 0.047117 | 0.003666 | -0.009026 | 0.023987 | 0.017092 | 0.054573 | 0.033662 | 0.011547 | 0.055763 | -0.095319 | -0.038574 | -0.004198 | 0.034847 | 0.025529 | 0.010828 | -0.031726 | -0.024676 | -0.042850 | -0.042644 | -0.010894 | -0.038050 | 0.023143 | 0.007163 | 0.015572 | 0.025795 | 0.046396 | 0.041314 | 0.025701 | 0.048876 | 0.040610 | 0.022926 | 0.046080 | 0.040514 | -0.011516 | 0.013794 | 0.009000 | 0.017778 | 0.004821 | 0.002005 | -0.006516 | -0.012508 | -0.005016 | -0.014544 | -0.006316 | -0.015353 | -0.008215 | 0.015192 | -0.003785 | -0.006172 | -0.002079 | -0.007272 | -0.002508 | -0.008370 | -0.005676 | 0.025045 | 0.024951 | 0.024876 | -0.034156 | 0.018123 | 0.016412 | 0.052263 |
| 8 | 0.004575 | 0.001371 | 0.054416 | 0.031756 | 0.032623 | -0.028847 | -0.028899 | -0.025820 | 0.025905 | -0.005059 | -0.052909 | 0.029611 | 0.008970 | -0.025919 | 0.000614 | -0.046538 | 0.034183 | 0.003977 | -0.005494 | -0.036125 | 0.026555 | -0.028588 | -0.013377 | -0.006089 | 0.009622 | -0.009340 | -0.027353 | 0.049842 | 0.038466 | 0.020689 | -0.041130 | -0.011161 | -0.043494 | -0.039270 | 0.043494 | -0.060423 | 0.030521 | -0.048661 | -0.004741 | 0.015731 | 0.067221 | -0.014172 | -0.038386 | -0.005654 | -0.002006 | -0.014882 | 0.005802 | -0.034264 | -0.013529 | -0.014469 | 0.069434 | -0.017467 | 0.004129 | 0.074906 | 0.091321 | 0.029100 | 0.077619 | -0.032810 | 0.081971 | 0.063127 | 0.048022 | -0.024396 | 0.050471 | -0.005869 | -0.014530 | 0.018719 | -0.045459 | -0.056158 | 0.039438 | -0.008929 | -0.011726 | 0.072651 | 0.017227 | 0.010780 | -0.029867 | 0.000785 | 0.008371 | -0.010298 | -0.015978 | 0.014895 | -0.028683 | 0.025575 | 0.014608 | -0.006418 | 0.041356 | -0.060808 | 0.084197 | 0.051611 | -0.045398 | 0.013334 | -0.013062 | -0.046132 | 0.018970 | 0.076729 | -0.011881 | 0.006319 | -0.042774 | 0.052488 | 0.068520 | -0.017806 | -0.060099 | 0.091890 | 0.010117 | -0.025517 | 0.035140 | -0.044608 | 0.058638 | -0.036319 | -0.132610 | -0.114818 | -0.137430 | -0.138763 | -0.074317 | 0.052853 | -0.088070 | 0.103673 | 0.030107 | 0.072521 | 0.045425 | 0.136632 | -0.022784 | -0.004025 | -0.039534 | 0.016578 | 0.029703 | 0.038239 | 0.007388 | 0.032622 | 0.014506 | -0.014370 | 0.002613 | -0.005787 | 0.022294 | 0.026294 | -0.002761 | 0.036367 | -0.040609 | -0.001995 | 0.069271 | -0.003845 | 0.021486 | -0.016114 | -0.033692 | -0.025790 | -0.042744 | -0.047902 | -0.035198 | -0.030485 | -0.028930 | 0.028402 | -0.041568 | -0.053099 | -0.037270 | 0.058343 | 0.011907 | 0.054277 | 0.019564 | 0.054280 | 0.095102 | 0.043171 | -0.042734 | 0.023521 | 0.085048 | -0.012670 | -0.106505 | 0.035857 | 0.007365 | -0.041430 | -0.001704 | 0.002341 | 0.030873 | 0.046488 | 0.019090 | -0.001221 | -0.041537 | -0.024395 | -0.015446 | -0.040680 | 0.023419 | 0.044138 | -0.045459 | 0.005303 | -0.007845 | -0.045459 | 0.045311 | -0.017537 | -0.010775 | 0.027398 | 0.024215 | -0.017487 | -0.001520 | 0.021299 | -0.003584 | 0.021931 | 0.047316 | 0.025648 | 0.025659 | -0.031174 | 0.076819 | 0.007075 | -0.007043 | -0.034543 | -0.070768 | 0.067888 | 0.112059 | 0.092431 | 0.084568 | 0.054611 | 0.034887 | 0.024780 | -0.063498 | -0.045182 | 0.185167 | -0.185374 | -0.027124 | -0.057671 | 0.022149 | 0.032577 | 0.026682 | 0.008633 | 0.032620 | 0.016949 | -0.045265 | 0.004526 | -0.010399 | 0.022195 | 0.026847 | -0.002505 | 0.036038 | -0.044424 | -0.001970 | 0.070466 | -0.003311 | 0.020971 | -0.017987 | -0.032694 | -0.026091 | -0.042794 | -0.048798 | -0.033384 | -0.029857 | -0.028054 | 0.029672 | -0.040141 | -0.051278 | -0.036667 | 0.061902 | 0.008983 | 0.053549 | 0.026595 | 0.053548 | 0.092009 | 0.041609 | -0.046811 | 0.035549 | 0.088166 | -0.008016 | -0.112011 | 0.037501 | 0.007261 | -0.045830 | -0.001304 | -0.018228 | 0.047886 | 0.053177 | 0.027077 | 0.022247 | 0.031296 | -0.013634 | -0.015376 | -0.047341 | 0.032507 | 0.045590 | -0.045459 | 0.014887 | -0.008018 | -0.045459 | 0.045361 | -0.021442 | -0.011852 | 0.027855 | 0.030593 | -0.022535 | -0.004602 | 0.019540 | -0.001727 | 0.025616 | 0.051178 | 0.026010 | 0.021552 | -0.031786 | 0.079765 | 0.012403 | -0.010845 | 0.005586 | 0.024525 | -0.037751 | -0.078579 | 0.061924 | 0.112441 | 0.092517 | 0.089141 | 0.054651 | 0.034883 | 0.021564 | -0.066992 | -0.043311 | 0.187947 | -0.192879 | -0.020820 | -0.038834 | 0.017097 | 0.028957 | 0.035284 | -0.004364 | 0.051162 | 0.018064 | -0.011828 | 0.005303 | -0.011062 | -0.008676 | 0.025186 | -0.003311 | 0.035468 | -0.036617 | -0.000889 | 0.069948 | -0.004489 | 0.018032 | -0.015782 | -0.031221 | -0.028352 | -0.043006 | -0.024705 | -0.026727 | -0.025663 | -0.025524 | 0.030973 | -0.042132 | -0.054808 | -0.036521 | 0.062566 | 0.012810 | 0.055744 | 0.023949 | 0.051902 | 0.096751 | 0.042742 | -0.042035 | 0.016641 | 0.085208 | -0.007090 | -0.104690 | 0.040987 | 0.008908 | -0.041750 | 0.042885 | 0.000890 | -0.048543 | 0.047393 | 0.015865 | -0.030936 | -0.099266 | -0.038397 | -0.009111 | -0.042265 | -0.014754 | 0.045558 | -0.045459 | 0.000292 | -0.022660 | -0.023263 | 0.000888 | 0.007072 | 0.013192 | -0.009959 | 0.004889 | 0.007633 | -0.007303 | -0.004031 | 0.020844 | 0.047467 | 0.025933 | 0.024294 | -0.020790 | 0.073569 | -0.024321 | -0.004256 | 0.042142 | -0.007578 | 0.078648 | 0.111633 | 0.065935 | 0.083124 | 0.054437 | 0.038019 | 0.025421 | -0.064116 | -0.043332 | 0.182106 | -0.184517 | -0.027011 | -0.007515 | -0.023217 | -0.021372 | -0.023494 | -0.049645 | -0.008444 | 0.003122 | -0.038875 | 0.030763 | 0.031717 | -0.038220 | 0.031370 | 0.031595 | -0.018782 | 0.031268 | 0.032124 | 0.013946 | 0.037645 | 0.028034 | 0.038743 | 0.010645 | 0.003586 | 0.007639 | 0.013296 | 0.007858 | 0.010549 | 0.005947 | 0.016058 | -0.030322 | 0.019811 | -0.013716 | -0.014297 | -0.011602 | -0.012801 | -0.010489 | -0.017497 | 0.017449 | -0.017125 | -0.017395 | -0.017257 | -0.016955 | -0.001162 | 0.004460 | 0.028351 |
| 9 | 0.005608 | -0.000348 | 0.050497 | -0.035992 | 0.238174 | -0.026712 | -0.128637 | -0.009127 | 0.009539 | -0.002315 | -0.033388 | -0.010388 | -0.048995 | 0.007304 | -0.012137 | -0.034984 | -0.001553 | -0.053719 | 0.000427 | 0.015014 | 0.008376 | -0.019988 | -0.011364 | 0.035412 | 0.030944 | 0.035316 | -0.047628 | 0.019145 | 0.037761 | -0.039153 | 0.043721 | 0.058146 | 0.047961 | 0.031599 | -0.047961 | 0.015531 | 0.006344 | 0.032161 | -0.019430 | 0.005657 | -0.052959 | -0.055135 | 0.074505 | 0.029299 | -0.024166 | -0.062518 | -0.003247 | -0.000385 | 0.024481 | 0.029069 | -0.051970 | 0.037649 | -0.014284 | -0.008889 | 0.034285 | -0.034475 | -0.086971 | 0.058897 | -0.022325 | 0.013267 | 0.002287 | 0.031856 | -0.005576 | -0.031658 | 0.017432 | 0.014614 | 0.000680 | -0.019579 | -0.034023 | -0.010697 | -0.003333 | 0.025314 | -0.016614 | -0.007716 | -0.002785 | -0.025174 | -0.002807 | -0.013879 | 0.010687 | 0.003808 | -0.010568 | -0.011866 | -0.013897 | -0.000733 | 0.025533 | 0.001638 | 0.029355 | -0.004076 | -0.003000 | -0.013699 | 0.008629 | 0.000179 | -0.007667 | 0.056706 | 0.012717 | 0.000769 | -0.025172 | -0.011005 | 0.013627 | 0.037926 | 0.060212 | -0.092819 | 0.003014 | -0.012467 | 0.007795 | -0.007717 | 0.035055 | -0.009453 | 0.057891 | -0.026320 | 0.067636 | 0.052601 | -0.025526 | 0.000355 | -0.036774 | -0.016659 | 0.024173 | 0.004696 | -0.038036 | -0.028437 | 0.016261 | 0.000016 | -0.035554 | 0.115437 | 0.121092 | 0.105713 | 0.068579 | 0.238527 | 0.239755 | -0.049833 | -0.045304 | 0.025895 | 0.012660 | 0.010680 | -0.014910 | -0.031319 | 0.013971 | -0.016423 | 0.059203 | -0.009056 | 0.060347 | -0.005576 | -0.004230 | -0.005128 | -0.013023 | -0.006531 | -0.005237 | -0.010431 | -0.010246 | 0.000600 | 0.004318 | 0.028273 | 0.032464 | 0.009299 | 0.039132 | -0.018696 | -0.036736 | -0.018698 | 0.043855 | 0.030222 | -0.025852 | -0.067037 | -0.019887 | -0.020628 | 0.062485 | -0.000829 | 0.020621 | 0.049647 | 0.003210 | 0.006021 | -0.003523 | -0.021725 | -0.006821 | 0.002155 | 0.009662 | 0.001264 | -0.008486 | 0.011040 | -0.007797 | -0.008309 | 0.000680 | 0.008566 | -0.023308 | 0.000680 | 0.044193 | 0.032087 | 0.025167 | 0.032202 | 0.006277 | -0.007455 | -0.007447 | 0.037379 | 0.039718 | 0.022399 | 0.024390 | -0.015604 | -0.007776 | 0.003966 | 0.071890 | 0.040454 | 0.027492 | 0.012067 | -0.055069 | 0.015312 | -0.094694 | -0.095492 | 0.046543 | 0.012928 | 0.009309 | 0.026205 | -0.042338 | -0.032546 | -0.040970 | 0.047954 | -0.044331 | -0.037416 | 0.121281 | 0.123862 | 0.114079 | 0.077295 | 0.238526 | 0.241575 | -0.038688 | -0.042738 | 0.021432 | 0.016102 | 0.010552 | -0.015335 | -0.030074 | 0.013257 | -0.016395 | 0.060025 | -0.010129 | 0.055726 | -0.007287 | -0.005293 | -0.005591 | -0.013712 | -0.005754 | -0.005298 | -0.010330 | -0.009898 | -0.001900 | 0.006108 | 0.028315 | 0.035950 | 0.005469 | 0.038950 | -0.017158 | -0.029939 | -0.017160 | 0.047187 | 0.033952 | -0.024814 | -0.062910 | -0.021160 | -0.020350 | 0.064469 | 0.002380 | 0.018821 | 0.051906 | 0.003401 | 0.020001 | -0.004525 | -0.022051 | -0.002861 | -0.000105 | 0.002532 | 0.000637 | -0.007729 | 0.012840 | -0.008774 | -0.009858 | 0.000680 | 0.010794 | -0.025002 | 0.000680 | 0.048615 | 0.030719 | 0.024453 | 0.031636 | 0.008751 | -0.008494 | -0.014677 | 0.035695 | 0.040740 | 0.021334 | 0.024804 | -0.015470 | -0.009766 | 0.003917 | 0.076911 | 0.040062 | 0.029102 | 0.025418 | 0.034141 | 0.012164 | -0.055036 | -0.010751 | -0.093462 | -0.095387 | 0.049167 | 0.012851 | 0.009382 | 0.023323 | -0.041461 | -0.035920 | -0.042509 | 0.050064 | -0.040067 | -0.036157 | 0.116455 | 0.119578 | 0.103170 | 0.088304 | 0.229093 | 0.237699 | -0.047691 | -0.045093 | -0.012072 | -0.000070 | 0.009276 | -0.014263 | 0.004152 | 0.014498 | -0.015305 | 0.059462 | -0.009880 | 0.060588 | -0.005694 | -0.013305 | -0.013538 | -0.011725 | 0.015815 | -0.016419 | -0.017337 | -0.017137 | 0.006148 | 0.007683 | 0.006959 | 0.034680 | 0.005645 | 0.026317 | -0.020924 | -0.039590 | -0.015617 | 0.043368 | 0.030136 | -0.026568 | -0.061646 | -0.017952 | -0.029899 | 0.060042 | 0.001326 | 0.022334 | 0.049860 | 0.001892 | 0.006517 | -0.011449 | -0.020048 | -0.003291 | -0.000668 | 0.048422 | -0.000184 | -0.006461 | 0.009116 | -0.008209 | -0.005747 | 0.000680 | 0.008592 | -0.019589 | -0.005728 | -0.033160 | -0.004997 | 0.016092 | -0.010432 | -0.009526 | -0.017779 | 0.012341 | 0.041863 | 0.022363 | 0.024419 | -0.015312 | -0.007871 | 0.008157 | 0.072827 | -0.014056 | 0.009268 | 0.046854 | 0.020746 | 0.000533 | -0.094563 | -0.062967 | 0.048673 | 0.012559 | 0.010127 | 0.026163 | -0.042712 | -0.032233 | -0.040696 | 0.047712 | -0.044881 | -0.016576 | 0.028936 | 0.049260 | 0.032330 | 0.003014 | 0.014068 | -0.006623 | 0.023894 | 0.045877 | 0.034166 | 0.024868 | 0.041653 | 0.033997 | 0.036892 | 0.044902 | 0.034731 | -0.023586 | -0.019481 | -0.023371 | -0.015811 | 0.029012 | 0.010163 | 0.034514 | 0.039256 | 0.033809 | 0.037596 | 0.032983 | 0.040089 | 0.020759 | -0.049630 | -0.080615 | -0.091975 | -0.080127 | -0.091392 | -0.080530 | -0.084917 | 0.016224 | 0.007084 | 0.008552 | 0.007046 | -0.014187 | -0.016451 | -0.016674 | 0.014406 |
| 10 | -0.013158 | -0.008889 | 0.010816 | 0.034775 | 0.118092 | -0.043839 | -0.023733 | -0.038060 | -0.004550 | -0.010033 | 0.025080 | -0.013281 | 0.020518 | -0.008505 | 0.008061 | 0.044372 | -0.022392 | 0.036633 | 0.029428 | -0.055577 | 0.002938 | 0.020648 | 0.029098 | -0.059532 | -0.037044 | -0.059348 | 0.026487 | 0.027644 | -0.011884 | 0.073207 | -0.030680 | -0.049850 | -0.043351 | -0.025902 | 0.043351 | 0.002023 | 0.017590 | -0.015763 | 0.027148 | -0.028167 | 0.006079 | 0.037748 | -0.026841 | -0.014844 | 0.019562 | 0.039565 | 0.000685 | 0.021923 | -0.006850 | -0.016164 | 0.004337 | -0.010180 | 0.027562 | 0.012421 | 0.009480 | 0.007546 | -0.000390 | 0.015780 | -0.003606 | -0.047628 | -0.042144 | 0.015333 | 0.008613 | -0.003036 | 0.017424 | 0.016282 | -0.025939 | 0.029522 | 0.043244 | 0.025670 | 0.003395 | -0.029879 | 0.025431 | 0.028566 | -0.009447 | 0.017224 | -0.005697 | 0.011021 | 0.010596 | 0.000430 | 0.012852 | -0.003251 | 0.003971 | 0.032999 | 0.015839 | 0.047327 | -0.042246 | -0.037757 | 0.043397 | 0.000490 | -0.005810 | 0.035428 | -0.003919 | -0.031161 | 0.000783 | -0.031458 | -0.016170 | 0.007973 | 0.000720 | -0.009304 | 0.032609 | -0.051830 | -0.004706 | -0.006665 | -0.014037 | -0.029328 | -0.048214 | 0.025137 | -0.101215 | 0.035384 | -0.100163 | -0.100577 | 0.023622 | 0.015413 | 0.057274 | 0.039050 | -0.003341 | -0.024320 | 0.018307 | 0.065791 | -0.042736 | 0.021963 | 0.038087 | -0.078852 | -0.094962 | 0.021840 | 0.069747 | 0.118176 | 0.119943 | 0.019331 | -0.018005 | -0.013189 | -0.025940 | -0.007721 | 0.003029 | 0.016072 | -0.019080 | 0.004556 | -0.035354 | -0.000882 | -0.051075 | -0.024789 | -0.004387 | -0.016076 | 0.017908 | -0.040792 | -0.005673 | 0.000877 | -0.003402 | -0.056570 | -0.118383 | -0.115522 | -0.075832 | -0.036821 | -0.031144 | -0.022136 | 0.010062 | -0.022139 | -0.064306 | -0.057036 | 0.051425 | 0.019271 | -0.010816 | 0.031285 | -0.005570 | 0.020554 | 0.021075 | 0.015224 | 0.011943 | -0.075460 | 0.010764 | 0.009296 | 0.010683 | 0.010973 | -0.021980 | 0.000921 | -0.008914 | -0.014538 | -0.002649 | 0.005590 | -0.025939 | 0.000991 | -0.008622 | -0.025939 | -0.025162 | 0.003928 | -0.034645 | -0.055581 | -0.000543 | 0.009786 | 0.033609 | -0.042035 | -0.021988 | -0.034773 | -0.036489 | -0.013027 | -0.022134 | 0.009812 | -0.138957 | -0.069598 | -0.063135 | 0.010585 | 0.029181 | -0.035221 | -0.076574 | -0.051888 | -0.140310 | 0.015372 | -0.014163 | -0.033685 | 0.051127 | 0.015377 | 0.104869 | -0.102181 | -0.018466 | 0.042326 | -0.081880 | -0.095836 | 0.028918 | 0.074620 | 0.118181 | 0.125284 | 0.015995 | -0.019564 | -0.008755 | -0.018226 | -0.008248 | 0.003100 | 0.013076 | -0.021964 | 0.004554 | -0.035573 | -0.000658 | -0.047683 | -0.022452 | -0.004566 | -0.016594 | 0.017284 | -0.041265 | -0.005180 | 0.000998 | -0.002243 | -0.056735 | -0.120673 | -0.117756 | -0.075056 | -0.039562 | -0.030331 | -0.022819 | 0.003783 | -0.022818 | -0.068507 | -0.060872 | 0.051852 | 0.012970 | -0.017309 | 0.032981 | -0.006864 | 0.022096 | 0.019554 | 0.011619 | 0.009265 | -0.065227 | 0.015101 | 0.010345 | 0.011134 | 0.015437 | 0.017777 | -0.001988 | -0.013225 | -0.016657 | 0.001416 | 0.006804 | -0.025939 | 0.005221 | -0.007221 | -0.025939 | -0.031828 | 0.001892 | -0.036128 | -0.054261 | 0.002835 | 0.011735 | 0.044835 | -0.040107 | -0.021506 | -0.038202 | -0.036094 | -0.012038 | -0.022225 | 0.009706 | -0.139377 | -0.075362 | -0.066195 | -0.072544 | -0.071497 | 0.007532 | 0.030663 | -0.040688 | -0.077067 | -0.052063 | -0.142265 | 0.015338 | -0.014229 | -0.033626 | 0.048639 | 0.017509 | 0.106912 | -0.102129 | -0.025067 | 0.038456 | -0.078816 | -0.093013 | 0.020950 | 0.050206 | 0.120474 | 0.123779 | 0.018655 | -0.013214 | -0.018083 | -0.002496 | -0.007788 | 0.002596 | 0.002665 | -0.016209 | 0.003097 | -0.037993 | 0.000130 | -0.050063 | -0.024685 | -0.001949 | -0.006438 | 0.013343 | -0.041129 | 0.003286 | 0.003534 | 0.002824 | -0.058037 | -0.080928 | -0.098554 | -0.077041 | -0.034665 | -0.022575 | -0.019386 | 0.013157 | -0.025786 | -0.064334 | -0.057176 | 0.052202 | 0.018838 | -0.012903 | 0.035156 | -0.004574 | 0.016253 | 0.018614 | 0.014956 | -0.008424 | -0.075165 | 0.026913 | 0.009481 | 0.011299 | 0.004859 | -0.010926 | 0.028542 | 0.036420 | -0.016423 | 0.013590 | 0.007770 | -0.025939 | -0.001054 | -0.006295 | -0.016049 | 0.007180 | 0.005545 | -0.017805 | -0.015271 | 0.007441 | 0.011536 | -0.005305 | -0.022299 | -0.033643 | -0.036593 | -0.012383 | -0.021867 | 0.010573 | -0.137941 | -0.004196 | 0.019229 | -0.028943 | -0.006050 | -0.042636 | -0.076071 | -0.046869 | -0.140738 | 0.015601 | -0.012280 | -0.032528 | 0.052029 | 0.013811 | 0.104193 | -0.101360 | -0.022047 | 0.026769 | -0.018209 | -0.032802 | -0.022889 | 0.025735 | -0.011770 | 0.017584 | 0.011964 | -0.033025 | -0.029464 | 0.010459 | -0.033188 | -0.028357 | -0.004357 | -0.032363 | -0.030389 | 0.018673 | 0.039684 | 0.033113 | 0.038866 | -0.020522 | -0.019554 | -0.085349 | -0.057937 | -0.087852 | -0.057520 | -0.084268 | -0.059135 | 0.075975 | 0.026262 | -0.170548 | -0.157866 | -0.169997 | -0.157307 | -0.171725 | -0.167586 | -0.023864 | 0.006731 | 0.006644 | 0.006741 | 0.003210 | 0.006757 | 0.010113 | 0.016884 |
| 11 | -0.004873 | -0.002964 | -0.005343 | -0.017158 | 0.152497 | -0.023146 | -0.071835 | 0.007067 | 0.035926 | -0.006662 | -0.015853 | -0.009177 | 0.025839 | 0.004928 | 0.001972 | -0.015117 | -0.012348 | -0.021803 | -0.028773 | -0.040408 | 0.016036 | 0.008715 | 0.029207 | -0.041996 | -0.013010 | -0.041388 | -0.002566 | 0.020269 | -0.000178 | 0.066649 | 0.009773 | 0.019218 | 0.014106 | 0.010474 | -0.014106 | 0.048639 | -0.019827 | 0.019819 | 0.009927 | -0.015489 | -0.058266 | 0.011829 | -0.011085 | -0.043648 | -0.004467 | -0.007141 | -0.055839 | -0.023756 | 0.021636 | 0.021263 | -0.086899 | 0.046627 | -0.000343 | -0.002025 | -0.037032 | -0.067064 | 0.026136 | -0.012477 | -0.001320 | -0.000843 | -0.009776 | -0.049636 | 0.007081 | 0.004737 | -0.051637 | 0.018595 | -0.010793 | 0.038970 | 0.037720 | -0.007036 | 0.011105 | -0.035542 | 0.001259 | -0.010547 | -0.010050 | 0.004585 | -0.023119 | -0.002050 | -0.024778 | 0.007778 | 0.000339 | 0.006152 | -0.020671 | 0.019997 | -0.005260 | 0.034828 | -0.016886 | -0.027861 | 0.023349 | -0.001347 | 0.007530 | 0.019531 | 0.003293 | -0.030728 | -0.000869 | -0.019292 | 0.003426 | -0.013658 | -0.003107 | -0.006239 | -0.129530 | 0.236406 | 0.008346 | -0.026117 | -0.029007 | 0.039945 | -0.057032 | 0.032947 | -0.027652 | 0.090543 | -0.034683 | 0.004005 | 0.064550 | -0.063396 | 0.079486 | -0.063008 | -0.016791 | -0.097066 | 0.030791 | -0.088980 | -0.037934 | 0.012843 | 0.010950 | 0.004986 | -0.012242 | 0.009879 | 0.022138 | 0.152534 | 0.138024 | -0.055773 | 0.069811 | 0.002408 | -0.011157 | -0.001826 | 0.000576 | -0.002111 | -0.002388 | 0.000536 | -0.004445 | -0.000984 | 0.012280 | 0.017160 | -0.026218 | -0.010554 | 0.006840 | -0.031903 | -0.011461 | -0.018209 | -0.012579 | 0.004707 | -0.035037 | -0.038066 | -0.031202 | -0.084810 | 0.032465 | -0.011060 | 0.018484 | -0.011083 | -0.066705 | -0.029095 | 0.015214 | 0.013636 | -0.019277 | -0.024693 | 0.028834 | -0.066946 | -0.056712 | -0.050885 | -0.036269 | 0.026838 | -0.001583 | -0.002634 | -0.010115 | 0.011297 | 0.009377 | -0.003843 | -0.007938 | 0.017142 | 0.005211 | 0.003469 | -0.010793 | 0.003975 | 0.019446 | -0.010793 | -0.023198 | -0.013777 | -0.014925 | 0.014030 | -0.001374 | 0.025256 | 0.015358 | 0.022533 | -0.003840 | -0.033261 | 0.004011 | 0.019581 | 0.008931 | -0.003832 | 0.020013 | 0.025455 | 0.020662 | -0.012285 | 0.016916 | 0.009733 | 0.238451 | 0.241707 | 0.036743 | -0.007548 | -0.026553 | -0.027915 | 0.076443 | 0.037245 | -0.059530 | 0.016982 | 0.022862 | 0.009682 | 0.001495 | -0.012250 | 0.016214 | 0.029696 | 0.152536 | 0.141942 | -0.047276 | 0.072801 | 0.002599 | -0.010740 | -0.001579 | 0.000905 | -0.002257 | -0.004295 | 0.000489 | -0.004016 | 0.000225 | 0.014294 | 0.018759 | -0.026345 | -0.012012 | 0.006887 | -0.032402 | -0.014155 | -0.019100 | -0.013920 | 0.001056 | -0.035348 | -0.042731 | -0.033360 | -0.079693 | 0.030876 | -0.018394 | 0.011209 | -0.018398 | -0.066812 | -0.026642 | 0.015538 | 0.005367 | -0.015172 | -0.026878 | 0.036425 | -0.065736 | -0.056908 | -0.045610 | -0.032756 | 0.039244 | -0.001684 | -0.002117 | -0.009030 | 0.008618 | -0.013483 | 0.001323 | -0.007816 | 0.014514 | 0.008024 | 0.005415 | -0.010793 | 0.004849 | 0.020953 | -0.010793 | -0.029737 | -0.014174 | -0.015447 | 0.013214 | -0.004271 | 0.028574 | 0.016501 | 0.021724 | 0.002488 | -0.033846 | 0.003693 | 0.018687 | 0.016252 | -0.004659 | 0.012105 | 0.019631 | 0.022061 | 0.024394 | 0.012266 | -0.011644 | 0.018059 | 0.064148 | 0.237255 | 0.241768 | 0.035001 | -0.007608 | -0.026713 | -0.027839 | 0.075768 | 0.040814 | -0.062620 | 0.019364 | 0.023725 | 0.010573 | 0.005197 | -0.011880 | 0.009588 | 0.026649 | 0.141041 | 0.140619 | -0.056330 | 0.069641 | -0.007057 | 0.001512 | -0.001209 | 0.000642 | -0.035949 | -0.001908 | 0.000145 | -0.002554 | -0.000611 | 0.013710 | 0.017552 | -0.011714 | -0.004742 | -0.000008 | -0.026965 | -0.001227 | -0.005013 | -0.002609 | 0.005170 | -0.045584 | -0.038072 | -0.034628 | -0.085956 | 0.028376 | -0.011689 | 0.016313 | -0.010131 | -0.068150 | -0.028849 | 0.014961 | 0.019678 | -0.018523 | -0.024430 | 0.031127 | -0.064915 | -0.053333 | -0.048771 | -0.019455 | 0.026810 | 0.010896 | -0.000597 | -0.012908 | 0.015734 | 0.008107 | -0.004880 | -0.014911 | 0.017188 | 0.005224 | 0.004576 | -0.010793 | 0.005844 | 0.001113 | 0.013396 | 0.007946 | 0.008382 | 0.011252 | 0.033327 | 0.019685 | 0.016850 | 0.003036 | -0.004227 | -0.031871 | 0.004182 | 0.018522 | 0.008738 | -0.005677 | 0.019290 | 0.005610 | 0.004709 | -0.040389 | -0.008081 | 0.043497 | 0.237939 | 0.161200 | 0.033052 | -0.007574 | -0.025612 | -0.028513 | 0.076128 | 0.035255 | -0.055348 | 0.017403 | 0.020255 | 0.012219 | 0.041276 | 0.036060 | 0.040995 | 0.000528 | 0.003611 | -0.020491 | -0.009255 | -0.114927 | -0.119076 | -0.008156 | -0.110797 | -0.120827 | -0.017421 | -0.116003 | -0.118954 | -0.036388 | -0.043213 | -0.048495 | -0.040235 | 0.027865 | 0.001376 | -0.020050 | 0.001175 | -0.021549 | 0.000450 | -0.022159 | -0.003024 | -0.033921 | 0.001384 | 0.074737 | 0.076335 | 0.074943 | 0.080329 | 0.075736 | 0.076454 | 0.021765 | -0.005042 | -0.005428 | -0.005206 | -0.026548 | -0.002399 | -0.003301 | 0.020156 |
| 12 | -0.023621 | 0.025101 | -0.023930 | -0.053934 | 0.041554 | 0.041050 | 0.010824 | 0.013694 | -0.022072 | -0.010600 | 0.050209 | 0.009878 | -0.014488 | 0.014035 | 0.018208 | 0.056857 | -0.004378 | 0.005653 | 0.030510 | 0.054500 | -0.025864 | 0.000277 | -0.016219 | 0.086321 | 0.044428 | 0.090506 | 0.001109 | -0.074370 | -0.028995 | -0.098793 | -0.026995 | -0.036661 | -0.039750 | -0.045836 | 0.039749 | -0.022035 | 0.001394 | -0.003681 | -0.016742 | -0.013126 | 0.029024 | 0.049433 | -0.024282 | 0.012139 | 0.071870 | 0.104112 | 0.053392 | 0.090144 | 0.004582 | 0.007484 | 0.030833 | -0.034072 | -0.008162 | -0.033096 | -0.006372 | 0.026967 | 0.022551 | -0.023994 | 0.041319 | -0.003787 | 0.031454 | 0.000072 | -0.018386 | 0.017332 | 0.003063 | -0.012914 | 0.006753 | -0.002373 | -0.050550 | 0.024252 | -0.046317 | -0.001511 | 0.002330 | 0.016019 | -0.013569 | -0.005118 | 0.002578 | -0.004196 | 0.006840 | 0.003548 | 0.019655 | -0.016068 | -0.019649 | -0.001300 | -0.020192 | 0.069503 | -0.011688 | -0.067579 | 0.055786 | 0.003739 | 0.021574 | 0.053885 | 0.017951 | 0.016507 | -0.001600 | 0.001413 | 0.020862 | -0.024886 | -0.001412 | -0.040071 | -0.070306 | 0.151304 | -0.011128 | 0.015092 | 0.026574 | 0.025304 | -0.053149 | 0.044790 | 0.019200 | -0.069001 | -0.001386 | 0.022970 | -0.085282 | 0.059416 | -0.063889 | 0.037914 | 0.047886 | 0.085815 | 0.058016 | 0.068137 | 0.003192 | -0.000237 | 0.026781 | -0.079932 | -0.082754 | 0.040933 | -0.009908 | 0.041697 | 0.039878 | 0.055270 | -0.050303 | -0.034118 | -0.035043 | -0.007657 | 0.018934 | 0.028752 | 0.015362 | 0.019458 | -0.062998 | 0.015957 | -0.017539 | -0.010967 | 0.014807 | -0.002266 | 0.018578 | 0.072609 | -0.002771 | -0.004032 | -0.011298 | 0.015255 | 0.110274 | 0.134321 | 0.092965 | 0.041287 | -0.063387 | 0.022728 | 0.013177 | 0.022733 | -0.007492 | -0.002492 | 0.019484 | -0.039755 | -0.046556 | 0.038686 | 0.032646 | 0.048422 | 0.051087 | 0.057261 | 0.063065 | -0.042973 | -0.007835 | 0.001482 | 0.000943 | -0.005556 | 0.010013 | -0.001862 | 0.002519 | -0.003487 | -0.012545 | -0.003973 | 0.006753 | -0.008840 | 0.017482 | 0.006753 | -0.024113 | -0.017231 | 0.035430 | -0.020196 | -0.030010 | -0.042367 | 0.003989 | -0.010381 | -0.004509 | -0.029542 | 0.010095 | -0.024991 | -0.038966 | 0.021117 | 0.060557 | -0.019864 | -0.039593 | 0.028053 | 0.015433 | 0.021312 | 0.160819 | 0.154198 | 0.075497 | -0.016063 | 0.029411 | -0.013662 | 0.066886 | 0.050404 | -0.010671 | 0.079411 | 0.003144 | 0.034055 | -0.077830 | -0.081369 | 0.049417 | -0.010601 | 0.041695 | 0.041605 | 0.065958 | -0.048316 | -0.032347 | -0.033819 | -0.008168 | 0.018719 | 0.032425 | 0.015284 | 0.019413 | -0.065056 | 0.014839 | -0.011659 | -0.011801 | 0.012842 | -0.001884 | 0.019193 | 0.074835 | -0.003121 | -0.004659 | -0.011268 | 0.010238 | 0.112999 | 0.139056 | 0.091440 | 0.040242 | -0.063399 | 0.026731 | 0.011789 | 0.026729 | -0.014063 | -0.000583 | 0.021032 | -0.033035 | -0.055143 | 0.032393 | 0.024043 | 0.047589 | 0.049003 | 0.053729 | 0.064448 | -0.043049 | -0.009738 | -0.000403 | -0.002167 | -0.007643 | 0.012100 | -0.009060 | 0.001518 | -0.002660 | -0.014627 | -0.004940 | 0.006753 | -0.011135 | 0.020937 | 0.006753 | -0.022577 | -0.016733 | 0.036192 | -0.021656 | -0.030284 | -0.036960 | 0.001973 | -0.007258 | -0.003914 | -0.032765 | 0.009862 | -0.023108 | -0.033640 | 0.021779 | 0.065032 | -0.020225 | -0.033344 | -0.047329 | -0.057891 | 0.027927 | 0.016003 | 0.057803 | 0.161206 | 0.154250 | 0.081102 | -0.016023 | 0.029432 | -0.017313 | 0.069074 | 0.054750 | -0.009263 | 0.080324 | -0.000631 | 0.026800 | -0.078782 | -0.083717 | 0.043174 | 0.019501 | 0.039425 | 0.033869 | 0.054561 | -0.052012 | 0.002485 | -0.024539 | -0.006805 | 0.018589 | 0.054458 | 0.011292 | 0.015848 | -0.065071 | 0.016516 | -0.015184 | -0.011945 | 0.010984 | 0.002531 | 0.007489 | 0.034050 | -0.003853 | -0.000907 | -0.003730 | 0.015582 | 0.125528 | 0.133620 | 0.095730 | 0.044113 | -0.056966 | 0.025018 | 0.018078 | 0.019702 | -0.006674 | -0.002448 | 0.020077 | -0.043578 | -0.049029 | 0.043390 | 0.031600 | 0.029495 | 0.043913 | 0.055099 | -0.025194 | -0.042251 | -0.001693 | -0.000070 | 0.001471 | 0.002539 | -0.058018 | -0.000527 | -0.007206 | -0.002431 | 0.026094 | -0.008231 | 0.006753 | -0.008497 | 0.032754 | -0.002715 | -0.042007 | -0.013163 | -0.008780 | 0.003825 | -0.026848 | -0.005346 | 0.034519 | -0.003077 | -0.030059 | 0.010490 | -0.024256 | -0.038218 | 0.016045 | 0.062317 | 0.008834 | -0.004935 | 0.023121 | -0.006796 | 0.043879 | 0.160602 | 0.115550 | 0.075058 | -0.016307 | 0.027203 | -0.014230 | 0.067121 | 0.047809 | -0.015012 | 0.079431 | 0.003265 | -0.047093 | -0.030628 | -0.000649 | -0.025621 | 0.064330 | 0.025570 | 0.044121 | 0.052533 | 0.127541 | 0.133464 | 0.052109 | 0.126961 | 0.135235 | 0.046448 | 0.127598 | 0.131665 | 0.009550 | 0.016382 | 0.023291 | 0.015893 | -0.028175 | -0.012747 | -0.041104 | -0.035027 | -0.042784 | -0.036336 | -0.038155 | -0.035632 | 0.059067 | 0.013455 | -0.114920 | -0.115807 | -0.115727 | -0.112552 | -0.116831 | -0.120960 | -0.003413 | 0.006338 | 0.005393 | 0.006184 | 0.004415 | -0.026404 | -0.028679 | -0.016444 |
| 13 | -0.011875 | 0.006592 | 0.020324 | -0.030426 | 0.108808 | 0.018450 | -0.028647 | 0.023571 | 0.005785 | 0.013102 | -0.041688 | 0.022156 | -0.007465 | 0.000643 | -0.013947 | -0.049339 | 0.038003 | 0.021928 | -0.007070 | -0.017625 | 0.032887 | 0.015565 | 0.012214 | 0.031779 | 0.043856 | 0.035768 | -0.064384 | 0.053547 | 0.072700 | -0.018970 | -0.056913 | -0.053174 | -0.079970 | -0.067887 | 0.079970 | -0.001325 | 0.060697 | -0.042984 | -0.003635 | 0.005876 | 0.052088 | -0.002056 | -0.028392 | 0.007703 | 0.034983 | 0.059373 | 0.042849 | 0.024662 | 0.027246 | 0.023263 | 0.053072 | -0.004102 | 0.009262 | 0.024224 | 0.032237 | 0.040669 | 0.026939 | -0.036886 | 0.032143 | -0.000185 | 0.026588 | -0.004412 | -0.017562 | 0.011039 | -0.002261 | -0.050086 | 0.036385 | -0.052327 | -0.004317 | -0.002510 | -0.027916 | 0.040946 | -0.001260 | 0.002879 | 0.017642 | -0.018940 | -0.002479 | 0.010670 | 0.000416 | -0.006360 | -0.029468 | 0.002390 | 0.009038 | -0.004296 | -0.002227 | 0.015457 | 0.025401 | -0.022879 | 0.028706 | 0.011859 | -0.010827 | 0.023421 | 0.013570 | 0.039408 | -0.010107 | 0.004648 | 0.002907 | 0.016390 | -0.016084 | 0.007930 | 0.016373 | -0.078843 | 0.018254 | 0.018728 | -0.021923 | 0.017782 | -0.021490 | 0.020733 | -0.036294 | -0.038630 | -0.034821 | -0.034031 | -0.070750 | 0.025165 | -0.029729 | 0.025805 | 0.065752 | 0.042067 | 0.004167 | 0.062350 | -0.016471 | -0.008728 | -0.009286 | -0.039273 | -0.026355 | 0.096741 | -0.023368 | 0.108852 | 0.075106 | 0.049157 | -0.044342 | -0.016610 | 0.013246 | 0.007358 | -0.005234 | 0.009969 | 0.014132 | -0.005958 | 0.028300 | -0.003872 | -0.020467 | 0.013208 | -0.001594 | 0.015250 | 0.034438 | -0.001436 | -0.023110 | -0.023367 | -0.026166 | 0.065727 | 0.078302 | 0.072934 | 0.014746 | -0.055729 | -0.042136 | 0.064274 | 0.040875 | 0.064270 | -0.073762 | -0.122499 | 0.147599 | -0.038138 | 0.024442 | 0.010850 | -0.025847 | 0.048997 | 0.038929 | 0.009719 | 0.053346 | 0.048862 | -0.005468 | -0.006630 | -0.004746 | -0.009666 | -0.004976 | 0.006002 | 0.020487 | -0.003622 | 0.001272 | -0.001697 | 0.036385 | -0.016741 | -0.002255 | 0.036385 | -0.016354 | 0.010970 | -0.008519 | 0.029557 | -0.012654 | 0.004343 | 0.016268 | -0.004287 | 0.016933 | -0.032052 | 0.020773 | -0.006257 | -0.033338 | -0.017508 | -0.055325 | -0.037102 | -0.045134 | -0.027786 | -0.000083 | 0.024640 | -0.083770 | -0.083139 | -0.076312 | -0.019094 | -0.023537 | -0.017643 | 0.024146 | 0.051110 | 0.029909 | -0.035395 | -0.009492 | -0.013240 | -0.034327 | -0.027962 | 0.091580 | -0.024951 | 0.108850 | 0.078636 | 0.051150 | -0.047039 | -0.023528 | 0.016806 | 0.007640 | -0.005449 | 0.008826 | 0.018568 | -0.006028 | 0.028678 | -0.004262 | -0.018951 | 0.011580 | -0.002982 | 0.015606 | 0.032837 | -0.001715 | -0.022620 | -0.023459 | -0.026031 | 0.056508 | 0.078319 | 0.073607 | 0.017284 | -0.065956 | -0.040076 | 0.059738 | 0.030928 | 0.059730 | -0.090397 | -0.121217 | 0.149150 | -0.029411 | 0.021421 | 0.011965 | -0.028693 | 0.050014 | 0.038672 | 0.006861 | 0.054716 | 0.032940 | -0.008794 | -0.007105 | -0.006658 | -0.011816 | -0.002768 | -0.003929 | 0.024749 | 0.002669 | -0.004962 | -0.003217 | 0.036385 | -0.021581 | 0.000202 | 0.036385 | -0.014030 | 0.012792 | -0.008759 | 0.026710 | -0.012965 | 0.002426 | 0.023135 | -0.004981 | 0.013123 | -0.034979 | 0.020774 | -0.005312 | -0.033971 | -0.016083 | -0.052238 | -0.031502 | -0.046129 | -0.045198 | -0.039445 | -0.028605 | -0.005630 | 0.002650 | -0.083321 | -0.083064 | -0.074931 | -0.019079 | -0.023581 | -0.021230 | 0.026218 | 0.053394 | 0.031827 | -0.032868 | -0.005750 | -0.008604 | -0.038657 | -0.026285 | 0.096712 | -0.003119 | 0.116819 | 0.074262 | 0.049623 | -0.047298 | 0.008051 | -0.015607 | 0.007226 | -0.005203 | 0.025119 | 0.013043 | -0.004291 | 0.030582 | -0.004902 | -0.021255 | 0.013077 | -0.022373 | -0.015620 | 0.020295 | 0.005543 | -0.033021 | -0.032797 | -0.032908 | 0.075937 | 0.013506 | 0.044086 | 0.015509 | -0.050530 | -0.035110 | 0.067652 | 0.048259 | 0.059680 | -0.073304 | -0.122925 | 0.148915 | -0.043385 | 0.024065 | 0.015612 | -0.026474 | 0.042426 | 0.036453 | 0.007901 | 0.042423 | 0.047764 | -0.027860 | -0.007594 | -0.005037 | 0.000979 | -0.063843 | 0.004170 | -0.010522 | -0.000692 | -0.002839 | -0.009606 | 0.036385 | -0.016383 | 0.016915 | 0.000918 | 0.003900 | 0.038484 | 0.014313 | -0.007181 | 0.000302 | 0.024878 | 0.006908 | 0.018729 | -0.032817 | 0.020112 | -0.005861 | -0.032259 | -0.006973 | -0.055330 | 0.012114 | 0.007049 | 0.006304 | -0.015269 | 0.011749 | -0.083850 | -0.048955 | -0.078634 | -0.018693 | -0.023438 | -0.018074 | 0.024704 | 0.050535 | 0.028007 | -0.034953 | -0.008184 | -0.043352 | -0.008185 | 0.031572 | -0.002108 | 0.008062 | 0.009716 | 0.046711 | 0.009539 | 0.040841 | 0.029384 | 0.008503 | 0.039438 | 0.029691 | 0.007838 | 0.040024 | 0.027392 | -0.018514 | -0.020469 | -0.022659 | -0.018904 | -0.014532 | 0.039343 | -0.009596 | -0.018210 | -0.005181 | -0.019227 | -0.008645 | -0.032535 | -0.091150 | 0.017876 | 0.238177 | 0.235976 | 0.238425 | 0.235373 | 0.240261 | 0.235023 | 0.011740 | -0.002390 | -0.003733 | -0.002542 | -0.011316 | -0.010482 | -0.013554 | 0.001763 |
| 14 | 0.054504 | -0.035016 | 0.008861 | 0.062077 | -0.056409 | -0.045143 | 0.016751 | -0.018209 | 0.011571 | 0.020341 | 0.035316 | -0.020355 | 0.020981 | 0.034294 | 0.007173 | 0.036717 | -0.029753 | 0.011799 | -0.024684 | -0.018671 | -0.004210 | -0.021727 | -0.000095 | 0.003348 | 0.012777 | 0.007365 | -0.022955 | 0.042878 | 0.028736 | 0.014823 | 0.002714 | -0.000266 | -0.018721 | -0.007341 | 0.018722 | 0.041329 | 0.037132 | 0.001010 | -0.041135 | 0.028762 | -0.036669 | 0.042246 | -0.040708 | -0.054863 | 0.009988 | 0.024867 | -0.050379 | -0.015047 | -0.059728 | -0.059199 | 0.020123 | 0.006543 | 0.002864 | 0.018023 | 0.049098 | -0.043786 | 0.007949 | -0.014381 | -0.014747 | -0.040002 | -0.030133 | -0.039399 | -0.004859 | 0.012216 | -0.035280 | -0.018198 | -0.008576 | -0.041951 | -0.024845 | -0.015937 | 0.025821 | 0.033844 | 0.018480 | 0.038914 | 0.005398 | 0.020544 | 0.010798 | 0.010313 | 0.035043 | -0.008615 | -0.021671 | -0.026855 | 0.040869 | -0.013924 | -0.003768 | 0.037145 | -0.006656 | -0.033624 | 0.026600 | -0.010801 | 0.019141 | 0.022349 | -0.041078 | -0.004838 | 0.011189 | 0.013623 | 0.003993 | -0.002333 | -0.025527 | 0.002344 | -0.012463 | 0.058989 | -0.013089 | -0.011951 | -0.054742 | 0.037295 | -0.001167 | -0.008032 | -0.057883 | 0.088949 | -0.043816 | -0.041753 | 0.075333 | -0.084737 | 0.075908 | -0.083774 | -0.056481 | -0.094951 | -0.025737 | -0.046072 | -0.040463 | 0.034881 | -0.027251 | 0.226422 | 0.226201 | -0.068929 | 0.041470 | -0.056439 | -0.023618 | -0.065681 | 0.031610 | -0.026754 | -0.008668 | -0.008045 | 0.014360 | 0.037877 | 0.022090 | 0.011558 | -0.045067 | 0.019811 | -0.016814 | 0.041891 | -0.020643 | -0.038023 | 0.009046 | 0.002430 | -0.019465 | -0.019075 | -0.018688 | 0.011415 | -0.039321 | -0.040746 | -0.005293 | -0.020222 | -0.029603 | 0.036881 | 0.027799 | 0.036872 | -0.049641 | -0.113273 | 0.096573 | 0.006704 | 0.002834 | 0.043272 | -0.024781 | 0.018873 | 0.002602 | -0.006008 | 0.003973 | -0.020238 | 0.004815 | -0.001087 | 0.009651 | 0.014469 | -0.009141 | 0.023761 | 0.005765 | -0.010202 | -0.007299 | 0.000837 | -0.008576 | -0.004949 | 0.029106 | -0.008576 | 0.021184 | -0.008193 | -0.008793 | -0.032493 | 0.028272 | 0.006771 | 0.012630 | -0.038326 | -0.018039 | -0.033545 | -0.011608 | -0.045260 | -0.038455 | -0.023591 | -0.062234 | -0.044920 | -0.049836 | -0.010309 | -0.006432 | 0.029229 | 0.048976 | 0.060113 | -0.060952 | -0.001302 | -0.055551 | 0.017543 | -0.007364 | -0.021569 | -0.032990 | -0.032434 | 0.000915 | -0.021084 | 0.224825 | 0.227610 | -0.076704 | 0.041290 | -0.056437 | -0.029766 | -0.066202 | 0.035926 | -0.023584 | -0.008187 | -0.008067 | 0.014046 | 0.037770 | 0.019226 | 0.011538 | -0.045420 | 0.018886 | -0.021441 | 0.041194 | -0.020797 | -0.037769 | 0.006981 | 0.001148 | -0.021130 | -0.019645 | -0.019114 | 0.013751 | -0.040679 | -0.042364 | -0.007653 | -0.022490 | -0.025254 | 0.028426 | 0.029207 | 0.028426 | -0.049626 | -0.109123 | 0.096503 | -0.000678 | -0.006085 | 0.046936 | -0.022032 | 0.018231 | 0.001436 | -0.007712 | 0.009662 | -0.015901 | 0.004671 | -0.000365 | 0.004952 | 0.013481 | 0.003694 | 0.014608 | 0.000947 | -0.009956 | -0.006356 | -0.001115 | -0.008576 | -0.002731 | 0.028898 | -0.008576 | 0.017182 | -0.008403 | -0.010368 | -0.031454 | 0.032017 | 0.002615 | 0.011156 | -0.036340 | -0.018356 | -0.038003 | -0.006647 | -0.045922 | -0.043395 | -0.024885 | -0.061082 | -0.048680 | -0.050410 | -0.047292 | -0.044982 | -0.015218 | -0.012741 | 0.036473 | 0.047927 | 0.060176 | -0.058786 | -0.001260 | -0.055607 | 0.015942 | -0.006349 | -0.022471 | -0.033610 | -0.024363 | -0.001060 | -0.027798 | 0.224940 | 0.225869 | -0.070736 | 0.011091 | -0.054105 | -0.020363 | -0.067155 | 0.033818 | 0.011389 | 0.016122 | -0.007447 | 0.015542 | 0.030416 | 0.019187 | 0.011234 | -0.046567 | 0.019645 | -0.017959 | 0.042614 | -0.017108 | -0.024723 | 0.017999 | -0.015762 | -0.013394 | -0.013270 | -0.013833 | 0.015651 | -0.041494 | -0.043654 | -0.006762 | -0.020601 | -0.031158 | 0.037479 | 0.027857 | 0.036123 | -0.050767 | -0.113414 | 0.097079 | 0.010672 | 0.001412 | 0.049729 | -0.021020 | 0.018426 | 0.001741 | -0.004638 | 0.022132 | -0.019824 | 0.003639 | 0.000406 | 0.009671 | 0.021933 | 0.008945 | 0.049791 | 0.042253 | -0.009157 | 0.031397 | -0.000367 | -0.008576 | -0.004644 | 0.032280 | 0.026536 | -0.020447 | -0.010724 | -0.003187 | -0.013142 | 0.004659 | -0.011324 | -0.000676 | -0.020563 | -0.035028 | -0.012056 | -0.045737 | -0.037802 | -0.014436 | -0.059506 | -0.021183 | 0.019085 | -0.005561 | 0.002018 | 0.037655 | 0.048918 | 0.040260 | -0.061235 | -0.000934 | -0.054105 | 0.016231 | -0.007298 | -0.021089 | -0.030741 | -0.031618 | -0.006871 | 0.045271 | 0.038021 | 0.022914 | 0.032528 | 0.027263 | -0.024918 | 0.005563 | 0.094822 | 0.173795 | 0.170762 | 0.094998 | 0.163841 | 0.170111 | 0.112636 | 0.175775 | 0.171099 | 0.009543 | -0.039144 | -0.029499 | -0.042281 | 0.001720 | 0.018240 | 0.057906 | 0.059257 | 0.063762 | 0.059326 | 0.057846 | 0.058954 | 0.032462 | 0.020477 | -0.036961 | -0.008126 | -0.037808 | -0.008138 | -0.038672 | -0.017105 | -0.002089 | 0.029183 | 0.028393 | 0.029343 | 0.002184 | -0.010881 | -0.010442 | -0.007270 |
| 15 | -0.019639 | -0.006645 | 0.031679 | 0.040837 | -0.034902 | -0.035975 | -0.000048 | 0.036664 | 0.005219 | -0.021678 | -0.037091 | -0.039203 | -0.016151 | -0.011799 | -0.006970 | -0.024303 | -0.040093 | 0.030503 | 0.024415 | 0.018380 | -0.025300 | 0.003820 | -0.043737 | 0.048280 | 0.035360 | 0.052435 | -0.067929 | 0.064195 | 0.092579 | -0.052735 | 0.000335 | -0.039032 | 0.010339 | 0.032012 | -0.010339 | 0.022541 | 0.053249 | 0.021020 | -0.022061 | 0.005362 | -0.042057 | -0.176823 | 0.201643 | 0.129659 | -0.105792 | -0.192314 | 0.052109 | -0.042638 | 0.027604 | 0.048848 | -0.068949 | 0.021150 | -0.061743 | 0.047258 | -0.029961 | -0.006476 | -0.178813 | 0.108719 | -0.033989 | 0.113975 | 0.094190 | 0.071626 | -0.019016 | -0.038444 | 0.077527 | 0.003135 | 0.028507 | -0.026292 | 0.051753 | -0.012772 | 0.018807 | 0.052782 | -0.000491 | -0.002516 | 0.008308 | -0.045868 | -0.010358 | -0.014167 | -0.040906 | 0.010869 | 0.007664 | -0.026889 | 0.014697 | 0.008470 | -0.000352 | -0.003530 | 0.024391 | -0.001275 | 0.011259 | -0.004763 | -0.019113 | 0.008642 | -0.024066 | 0.024002 | 0.005104 | -0.007331 | 0.000966 | 0.005738 | -0.037142 | 0.023231 | -0.026666 | 0.031524 | -0.013389 | -0.052117 | 0.039599 | 0.008330 | 0.085789 | -0.035291 | -0.041844 | 0.058268 | -0.037102 | -0.039417 | 0.073991 | -0.036351 | 0.057438 | -0.016925 | -0.060926 | -0.073940 | -0.002599 | -0.011548 | 0.007891 | 0.021490 | -0.014161 | -0.052316 | -0.062802 | 0.011891 | -0.033333 | -0.035145 | -0.070841 | -0.035946 | 0.017964 | -0.049516 | -0.039479 | 0.007317 | -0.005605 | -0.036974 | 0.005051 | -0.005550 | 0.022586 | -0.004268 | -0.025780 | -0.058614 | 0.005404 | -0.003466 | 0.023343 | 0.045141 | -0.017282 | -0.013872 | -0.016278 | 0.042021 | 0.031218 | 0.039812 | 0.032513 | -0.030640 | 0.011495 | 0.015968 | -0.016892 | 0.015957 | -0.064663 | -0.086606 | 0.154682 | -0.070819 | 0.003720 | -0.125481 | 0.113523 | -0.088246 | -0.031223 | 0.021222 | -0.069784 | -0.040797 | -0.022849 | -0.023418 | -0.003295 | -0.032240 | 0.025014 | -0.046051 | -0.011027 | 0.044556 | -0.010493 | -0.015680 | 0.028507 | 0.022402 | 0.049397 | 0.028507 | 0.010561 | -0.006131 | -0.021186 | 0.016620 | -0.018393 | 0.021631 | 0.001397 | -0.000602 | 0.053613 | -0.038271 | 0.002099 | 0.035359 | 0.014501 | 0.010377 | -0.005416 | -0.003002 | -0.009909 | -0.002056 | 0.008709 | 0.031504 | 0.042346 | 0.031371 | 0.011395 | 0.032890 | 0.039570 | -0.011873 | -0.113159 | -0.070408 | 0.036922 | -0.086132 | -0.060882 | -0.009969 | -0.051438 | -0.060686 | -0.003451 | -0.034459 | -0.035145 | -0.069445 | -0.049495 | 0.013909 | -0.048760 | -0.042318 | 0.007431 | -0.005863 | -0.039743 | 0.008609 | -0.005576 | 0.023059 | -0.004857 | -0.028838 | -0.056487 | 0.003314 | -0.003370 | 0.023896 | 0.044827 | -0.014411 | -0.014067 | -0.016406 | 0.038792 | 0.028835 | 0.045201 | 0.033542 | -0.038525 | 0.012289 | 0.022043 | -0.017601 | 0.022034 | -0.074857 | -0.090964 | 0.153580 | -0.066171 | 0.014865 | -0.126403 | 0.123139 | -0.084306 | -0.029148 | 0.033300 | -0.071882 | -0.018826 | -0.018046 | -0.021597 | 0.001589 | -0.025552 | -0.019576 | -0.043327 | -0.000335 | 0.048036 | -0.017166 | -0.017082 | 0.028507 | 0.026146 | 0.045134 | 0.028507 | 0.009709 | -0.007909 | -0.020747 | 0.015054 | -0.016179 | 0.019934 | -0.001556 | -0.004038 | 0.051576 | -0.037257 | -0.000006 | 0.035699 | 0.013539 | 0.008708 | -0.001320 | -0.000928 | -0.012404 | -0.008852 | 0.005353 | 0.003546 | 0.007507 | 0.026034 | 0.042297 | 0.031474 | 0.015015 | 0.032751 | 0.039565 | -0.013200 | -0.109776 | -0.072388 | 0.039286 | -0.082932 | -0.054747 | -0.013260 | -0.051098 | -0.061669 | 0.009872 | -0.046327 | -0.029580 | -0.065856 | -0.033968 | 0.014894 | -0.023162 | 0.020197 | 0.006986 | -0.005137 | -0.036810 | 0.007856 | -0.001738 | 0.023837 | -0.003755 | -0.027042 | -0.058986 | 0.000137 | -0.004394 | 0.012487 | 0.041650 | -0.011243 | -0.009348 | -0.009118 | 0.050792 | -0.009338 | -0.002023 | 0.035640 | -0.031134 | 0.017491 | 0.015357 | -0.018694 | 0.017249 | -0.065176 | -0.087078 | 0.153837 | -0.067163 | 0.010493 | -0.155757 | 0.103494 | -0.065805 | -0.022819 | 0.020375 | -0.038973 | -0.041008 | -0.032077 | -0.021990 | 0.004626 | -0.048501 | 0.088805 | -0.105521 | -0.098463 | 0.041808 | -0.017044 | -0.016412 | 0.028507 | 0.022192 | 0.029236 | -0.000861 | 0.037368 | -0.003082 | -0.044661 | -0.006603 | 0.012271 | -0.018307 | -0.012271 | 0.057800 | -0.037835 | 0.002887 | 0.035607 | 0.013362 | 0.007885 | -0.002525 | -0.003508 | 0.006705 | 0.028728 | -0.007281 | 0.034060 | 0.042142 | 0.006882 | 0.011591 | 0.032757 | 0.043222 | -0.012075 | -0.113681 | -0.068624 | 0.038141 | -0.086167 | -0.065482 | 0.031344 | 0.012715 | 0.020404 | 0.008491 | -0.008710 | -0.019297 | 0.010016 | -0.016957 | 0.035805 | 0.025454 | -0.018634 | 0.035047 | 0.025495 | -0.019356 | 0.036469 | 0.025390 | 0.033781 | 0.066836 | 0.053243 | 0.066221 | 0.025508 | -0.053273 | -0.072169 | -0.041606 | -0.071335 | -0.044306 | -0.074084 | -0.026646 | 0.008001 | 0.076494 | -0.039086 | -0.028128 | -0.039139 | -0.029238 | -0.038610 | -0.045735 | -0.023017 | 0.100551 | 0.101571 | 0.100725 | -0.031989 | -0.012631 | -0.011904 | -0.001967 |
| 16 | -0.039588 | 0.031391 | -0.023387 | 0.000020 | 0.050174 | -0.012192 | -0.031490 | 0.005107 | 0.023064 | 0.007678 | -0.040645 | -0.008801 | 0.011174 | -0.000428 | -0.011822 | -0.036677 | 0.000176 | -0.033828 | 0.001289 | -0.008115 | -0.005838 | -0.004308 | -0.007070 | -0.036240 | -0.032584 | -0.041649 | 0.033746 | -0.016017 | -0.024633 | 0.039463 | -0.081610 | -0.067405 | -0.059998 | -0.059779 | 0.059999 | -0.099611 | -0.079725 | -0.076504 | 0.030096 | -0.042596 | 0.039649 | -0.048627 | 0.038895 | 0.054270 | 0.073899 | 0.016822 | 0.072735 | 0.076134 | -0.033367 | -0.023254 | 0.039908 | -0.056534 | -0.025941 | 0.005736 | -0.028273 | 0.033185 | -0.024156 | 0.016412 | 0.045800 | 0.035406 | 0.048702 | 0.029168 | 0.003212 | -0.011954 | 0.030570 | 0.003355 | 0.007375 | 0.023965 | 0.083467 | 0.010676 | 0.035979 | -0.024541 | 0.013376 | -0.009851 | 0.023506 | -0.003220 | -0.004086 | -0.007730 | -0.051951 | -0.014176 | -0.006081 | 0.021941 | -0.009529 | -0.030787 | -0.007614 | 0.030105 | -0.034283 | -0.026653 | 0.026159 | -0.018619 | -0.001827 | 0.031393 | -0.019064 | -0.020006 | 0.018231 | 0.031443 | 0.006718 | -0.029735 | -0.014995 | 0.017955 | -0.006130 | -0.015831 | 0.016002 | -0.015739 | -0.020288 | 0.021991 | 0.025628 | -0.003602 | -0.076714 | 0.163818 | -0.057922 | -0.057596 | 0.126657 | -0.106031 | 0.155357 | -0.087974 | -0.050565 | -0.168762 | -0.038622 | -0.115066 | -0.087643 | 0.009840 | 0.021328 | -0.080373 | -0.096543 | -0.061577 | -0.001743 | 0.050021 | 0.044856 | -0.064617 | -0.004028 | -0.065115 | -0.057077 | -0.003445 | -0.013299 | -0.022492 | 0.006586 | -0.015756 | 0.021178 | -0.006584 | 0.021377 | -0.011102 | -0.003073 | -0.013436 | -0.012058 | -0.025356 | 0.013124 | 0.016248 | 0.019370 | -0.045227 | -0.048064 | -0.052082 | -0.045255 | 0.061535 | -0.039006 | -0.035763 | -0.017171 | -0.035766 | 0.070814 | 0.105426 | -0.143806 | -0.045378 | -0.005425 | -0.048398 | -0.012014 | 0.010597 | -0.007434 | -0.012385 | 0.091612 | 0.068182 | -0.013463 | 0.003516 | 0.005431 | -0.020302 | 0.008111 | -0.042760 | -0.002693 | -0.005887 | 0.006699 | 0.004140 | 0.007375 | -0.000763 | 0.021169 | 0.007375 | -0.019169 | -0.003804 | 0.041095 | 0.001971 | -0.024751 | 0.043663 | -0.015694 | 0.024449 | -0.002668 | 0.018342 | 0.005282 | 0.088957 | 0.087286 | 0.011891 | -0.002444 | -0.038471 | -0.044041 | 0.008679 | 0.043973 | -0.071610 | -0.008160 | -0.014517 | 0.006458 | -0.017171 | -0.019510 | 0.016726 | -0.021009 | 0.015848 | -0.065367 | 0.000467 | -0.030374 | 0.017367 | -0.081831 | -0.093949 | -0.055223 | 0.001335 | 0.050028 | 0.047637 | -0.059036 | -0.000809 | -0.061485 | -0.057271 | -0.002844 | -0.012757 | -0.018979 | 0.006542 | -0.015732 | 0.022380 | -0.004069 | 0.020718 | -0.009596 | -0.000607 | -0.012581 | -0.011410 | -0.027909 | 0.013603 | 0.016521 | 0.019235 | -0.041264 | -0.050467 | -0.055015 | -0.049227 | 0.058144 | -0.043895 | -0.037836 | -0.016924 | -0.037832 | 0.079357 | 0.106163 | -0.143978 | -0.034336 | -0.001148 | -0.043093 | -0.012112 | 0.009312 | -0.007014 | -0.013233 | 0.091842 | 0.049184 | -0.005121 | 0.005653 | 0.007713 | -0.011162 | -0.002939 | -0.036773 | 0.002596 | -0.001875 | 0.005733 | 0.005205 | 0.007375 | 0.001278 | 0.018636 | 0.007375 | -0.019241 | -0.007087 | 0.040948 | 0.001519 | -0.022518 | 0.048384 | -0.023319 | 0.023378 | -0.000027 | 0.016647 | 0.010222 | 0.086096 | 0.089191 | 0.010658 | -0.000346 | -0.045226 | -0.044126 | -0.036601 | -0.043476 | 0.008730 | 0.045124 | -0.069433 | -0.007264 | -0.014626 | 0.010638 | -0.017343 | -0.019544 | 0.019325 | -0.023501 | 0.015837 | -0.065196 | 0.007453 | -0.030268 | 0.021775 | -0.078838 | -0.097850 | -0.061260 | -0.007173 | 0.047780 | 0.047442 | -0.065784 | -0.004650 | -0.003074 | 0.013438 | -0.003712 | -0.012445 | -0.041557 | 0.006825 | -0.014849 | 0.023102 | -0.007762 | 0.023427 | -0.011296 | 0.004658 | -0.000849 | -0.030949 | -0.017795 | 0.012880 | 0.013110 | 0.013451 | -0.051423 | -0.033244 | -0.038440 | -0.046678 | 0.069050 | -0.024934 | -0.032681 | -0.009487 | -0.040483 | 0.073758 | 0.106411 | -0.142938 | -0.050062 | -0.004052 | -0.055442 | -0.015122 | -0.006337 | -0.010474 | -0.014625 | 0.026807 | 0.067344 | -0.007672 | 0.003118 | 0.006563 | -0.030347 | -0.047522 | -0.065586 | -0.038669 | -0.006213 | -0.014584 | 0.001866 | 0.007375 | -0.002127 | 0.010873 | -0.029426 | 0.059610 | -0.023178 | -0.011238 | 0.022668 | 0.019508 | -0.021055 | -0.017241 | -0.002499 | 0.018786 | 0.005082 | 0.087683 | 0.088527 | 0.019424 | -0.004228 | -0.001143 | -0.012665 | -0.013018 | -0.016261 | -0.073758 | -0.008466 | -0.005353 | 0.007070 | -0.017187 | -0.020514 | 0.016477 | -0.021231 | 0.015829 | -0.059310 | 0.002048 | -0.031878 | 0.025462 | 0.034224 | 0.004075 | 0.032278 | 0.047586 | -0.023099 | 0.001801 | 0.036388 | 0.177362 | 0.192854 | 0.033953 | 0.170765 | 0.193230 | 0.024795 | 0.180902 | 0.193886 | 0.032248 | 0.007245 | 0.019995 | 0.002464 | 0.046334 | 0.009686 | -0.016740 | -0.002014 | -0.013747 | -0.000008 | -0.020534 | -0.008545 | -0.033182 | -0.025858 | 0.082311 | 0.061377 | 0.083182 | 0.058234 | 0.082558 | 0.071147 | -0.006744 | -0.026318 | -0.027006 | -0.026124 | -0.027500 | 0.031044 | 0.030963 | 0.045295 |
| 17 | -0.016545 | 0.020888 | 0.032085 | 0.017613 | -0.032923 | -0.008092 | 0.010321 | 0.031966 | 0.003715 | 0.015132 | 0.064988 | -0.006938 | -0.014576 | -0.005628 | 0.012727 | 0.070590 | -0.019659 | -0.043026 | -0.026241 | -0.031822 | -0.023163 | 0.002581 | 0.013477 | -0.023776 | -0.007743 | -0.024042 | -0.046117 | 0.083842 | 0.069121 | 0.032436 | -0.017470 | -0.013333 | -0.022880 | -0.013042 | 0.022880 | 0.007367 | 0.005682 | -0.034887 | -0.005255 | -0.044510 | -0.041272 | 0.019979 | -0.042401 | -0.055507 | 0.098100 | 0.060992 | -0.036356 | 0.098105 | -0.002077 | -0.004995 | -0.009701 | 0.046774 | 0.008921 | 0.030264 | 0.039146 | -0.037055 | -0.017371 | -0.005835 | 0.000529 | -0.058723 | -0.046533 | -0.026510 | -0.000832 | -0.022936 | -0.038898 | 0.034627 | -0.016622 | -0.038351 | -0.013629 | -0.015319 | 0.044309 | 0.001813 | 0.063328 | 0.013562 | 0.033443 | 0.011392 | -0.000921 | 0.037848 | -0.023801 | 0.022072 | 0.031534 | -0.039857 | -0.041637 | -0.017616 | 0.004874 | 0.006376 | 0.023882 | -0.013801 | 0.004671 | -0.006921 | 0.020086 | 0.001789 | 0.054313 | 0.011145 | 0.010633 | 0.018348 | -0.006339 | -0.004643 | 0.017193 | 0.000102 | 0.046339 | -0.042964 | -0.040193 | -0.006278 | 0.024126 | -0.018283 | -0.008577 | -0.025133 | 0.075782 | -0.020358 | 0.076903 | 0.050190 | -0.000082 | 0.017667 | -0.024914 | -0.020293 | -0.050224 | 0.034101 | -0.020811 | -0.026059 | 0.070657 | -0.014451 | 0.049951 | -0.074407 | -0.068598 | 0.018456 | 0.017136 | -0.033070 | -0.021883 | 0.029824 | -0.002389 | -0.032161 | -0.052637 | -0.013589 | 0.014476 | -0.011725 | -0.002762 | 0.010597 | -0.056785 | 0.024604 | 0.048012 | 0.050769 | -0.042086 | -0.051353 | -0.016224 | -0.025444 | -0.012005 | -0.017203 | -0.017361 | -0.038355 | -0.120895 | -0.119186 | -0.038732 | -0.074684 | 0.025006 | 0.011843 | 0.036982 | 0.011849 | -0.040598 | -0.091918 | 0.136925 | -0.064278 | -0.075236 | 0.035269 | 0.012453 | 0.041103 | 0.004768 | 0.037225 | 0.110028 | 0.031053 | 0.004210 | -0.010569 | 0.017953 | 0.024396 | 0.009566 | 0.018364 | -0.006386 | -0.012069 | -0.004846 | -0.020198 | -0.016622 | -0.003745 | -0.049412 | -0.016622 | 0.026160 | -0.008987 | -0.008721 | -0.038742 | 0.009489 | -0.026448 | 0.008674 | -0.003610 | -0.017042 | 0.012437 | 0.012310 | 0.044311 | 0.088080 | 0.051062 | 0.209941 | -0.056627 | -0.086995 | -0.021715 | 0.008612 | -0.033490 | -0.016081 | -0.042545 | 0.187203 | 0.029480 | 0.024909 | 0.007352 | -0.015192 | -0.092273 | -0.012383 | 0.022847 | 0.030886 | 0.043231 | -0.075840 | -0.070784 | 0.012475 | 0.017169 | -0.033069 | -0.021747 | 0.016854 | -0.008375 | -0.029981 | -0.047329 | -0.013975 | 0.013276 | -0.015489 | -0.004373 | 0.010623 | -0.058057 | 0.020647 | 0.034436 | 0.051048 | -0.039275 | -0.050373 | -0.015742 | -0.025805 | -0.012778 | -0.016740 | -0.016663 | -0.032819 | -0.126143 | -0.118955 | -0.043995 | -0.092511 | 0.027392 | 0.012172 | 0.023974 | 0.012199 | -0.040315 | -0.091121 | 0.135650 | -0.078437 | -0.076676 | 0.040213 | 0.016060 | 0.043071 | 0.002717 | 0.034750 | 0.114768 | 0.044206 | 0.000639 | -0.013098 | 0.013138 | 0.018583 | 0.009200 | 0.010982 | -0.011684 | -0.011359 | -0.001882 | -0.018516 | -0.016622 | -0.001373 | -0.046528 | -0.016622 | 0.031350 | -0.010819 | -0.008453 | -0.037164 | 0.008406 | -0.028732 | 0.010628 | 0.001260 | -0.019878 | 0.013570 | 0.018080 | 0.042801 | 0.089612 | 0.052547 | 0.206758 | -0.055345 | -0.084648 | -0.075980 | -0.081369 | -0.019891 | 0.004048 | -0.033293 | -0.012465 | -0.042505 | 0.188394 | 0.029553 | 0.024929 | 0.008221 | -0.013539 | -0.091563 | -0.012197 | 0.026780 | 0.028290 | 0.050667 | -0.073491 | -0.069389 | 0.016321 | 0.012581 | -0.024744 | -0.022106 | 0.029489 | -0.006768 | -0.021536 | -0.005225 | -0.013346 | 0.015987 | -0.003712 | -0.003487 | 0.010706 | -0.060427 | 0.023768 | 0.048812 | 0.049852 | -0.031322 | -0.034957 | 0.006367 | -0.022975 | -0.013774 | -0.016268 | -0.016283 | -0.030657 | -0.114029 | -0.128413 | -0.040181 | -0.072722 | 0.026872 | 0.012153 | 0.037776 | 0.011108 | -0.040595 | -0.091569 | 0.137915 | -0.060551 | -0.075926 | 0.042365 | 0.015503 | 0.018090 | -0.001401 | 0.038040 | 0.021815 | 0.030745 | -0.014858 | -0.009272 | 0.019199 | 0.035615 | 0.006163 | 0.055710 | 0.041088 | -0.012139 | -0.009150 | -0.014503 | -0.016622 | -0.002766 | -0.059609 | 0.007340 | -0.003293 | -0.012556 | 0.007866 | -0.015696 | 0.021529 | 0.004088 | 0.038956 | -0.016985 | 0.011957 | 0.010309 | 0.044135 | 0.085675 | 0.050310 | 0.213620 | -0.004305 | 0.034779 | -0.005930 | -0.015432 | -0.036709 | -0.016218 | -0.032340 | 0.197447 | 0.029636 | 0.026644 | 0.007309 | -0.014337 | -0.091425 | -0.012359 | 0.021684 | 0.033014 | 0.036893 | 0.078071 | 0.038007 | 0.075772 | -0.044343 | -0.015170 | -0.026189 | -0.077872 | 0.004186 | 0.001733 | -0.079956 | 0.006829 | 0.001746 | -0.089717 | 0.004767 | 0.003057 | 0.013863 | 0.032938 | 0.033781 | 0.031951 | -0.011332 | 0.045550 | 0.084387 | 0.080876 | 0.082628 | 0.084182 | 0.084436 | 0.079236 | 0.015753 | -0.015987 | 0.004402 | 0.000887 | 0.002298 | 0.003157 | 0.002186 | 0.005847 | -0.015084 | 0.021456 | 0.021312 | 0.021738 | -0.000447 | -0.014827 | -0.015615 | -0.004085 |
| 18 | 0.000398 | -0.002638 | 0.023343 | 0.055859 | 0.042304 | -0.038309 | 0.000392 | -0.017836 | 0.024401 | -0.009064 | 0.058520 | -0.003623 | -0.006193 | -0.033915 | 0.013091 | 0.065166 | -0.015828 | 0.058405 | -0.001855 | -0.009098 | 0.009843 | 0.012521 | -0.000576 | -0.027080 | -0.022003 | -0.025859 | 0.035615 | -0.010534 | -0.019822 | 0.028724 | 0.025894 | 0.027528 | 0.028224 | 0.026801 | -0.028224 | -0.011298 | -0.034914 | 0.015500 | 0.015209 | -0.011069 | -0.089119 | 0.010058 | 0.002581 | -0.060813 | -0.159929 | -0.115341 | -0.122343 | -0.155826 | -0.038417 | -0.038298 | -0.055509 | 0.024137 | 0.001636 | -0.041528 | -0.020305 | -0.067644 | -0.046847 | 0.033812 | -0.150984 | -0.081299 | -0.104581 | -0.010055 | -0.000080 | 0.011068 | -0.007439 | -0.007295 | -0.013457 | 0.023170 | 0.019565 | 0.006448 | 0.004136 | 0.008711 | 0.012944 | 0.042814 | 0.007441 | 0.006183 | 0.015251 | 0.032247 | 0.089728 | -0.001115 | 0.038215 | -0.086606 | -0.006260 | 0.023421 | 0.009351 | -0.028281 | -0.003495 | 0.030136 | -0.017346 | 0.047069 | -0.029218 | -0.015291 | 0.021869 | -0.029338 | -0.048718 | -0.024229 | -0.009844 | 0.041836 | 0.017424 | 0.017681 | 0.052177 | -0.027097 | -0.077100 | 0.041303 | 0.010001 | 0.001207 | -0.060122 | 0.031099 | -0.026738 | -0.059339 | -0.039776 | -0.033835 | -0.018443 | 0.061918 | -0.048667 | 0.063759 | 0.006930 | 0.054254 | 0.044603 | 0.069868 | -0.020725 | 0.030364 | 0.053439 | -0.052853 | -0.066159 | -0.003833 | 0.017110 | 0.042308 | 0.025825 | -0.058347 | 0.031864 | 0.016342 | 0.016972 | -0.021617 | 0.010157 | -0.042363 | -0.032864 | 0.010548 | -0.071742 | 0.007656 | -0.053226 | 0.018919 | 0.009144 | 0.025442 | 0.018874 | -0.014819 | 0.009129 | 0.016285 | 0.019865 | 0.005127 | -0.015096 | -0.049422 | -0.021729 | 0.042889 | 0.016824 | 0.013772 | 0.005773 | 0.013757 | 0.025356 | -0.017135 | -0.023171 | 0.044107 | 0.022125 | 0.028807 | 0.042746 | -0.079571 | -0.031446 | -0.021531 | -0.185970 | 0.006170 | -0.003205 | -0.006633 | 0.030385 | 0.025456 | -0.012890 | 0.027038 | 0.000484 | 0.023123 | 0.000637 | -0.010205 | -0.013457 | 0.000639 | 0.028554 | -0.013457 | -0.025936 | 0.017749 | -0.011605 | -0.027286 | -0.030830 | -0.026479 | 0.044087 | -0.020572 | 0.001896 | -0.000850 | 0.003929 | -0.111743 | -0.085511 | 0.029611 | 0.090999 | 0.090081 | 0.096606 | 0.005840 | 0.001327 | -0.067583 | -0.022042 | -0.029154 | 0.080766 | -0.063667 | 0.005764 | -0.055722 | 0.081392 | 0.008213 | 0.082279 | -0.019590 | 0.073000 | 0.057806 | -0.060543 | -0.066323 | -0.018391 | 0.019733 | 0.042305 | 0.028284 | -0.061785 | 0.034363 | 0.017944 | 0.019080 | -0.022103 | 0.010257 | -0.042035 | -0.032699 | 0.010548 | -0.072305 | 0.008052 | -0.050948 | 0.022268 | 0.010591 | 0.025290 | 0.018082 | -0.014307 | 0.010811 | 0.016501 | 0.019795 | 0.008846 | -0.012781 | -0.048359 | -0.023757 | 0.046054 | 0.018367 | 0.012745 | 0.015986 | 0.012734 | 0.027534 | -0.020334 | -0.024025 | 0.027557 | 0.022143 | 0.023576 | 0.049926 | -0.080134 | -0.031093 | -0.014515 | -0.182208 | 0.021140 | -0.013885 | -0.005644 | 0.024268 | 0.008819 | -0.012665 | 0.031717 | -0.004411 | 0.016170 | 0.004776 | -0.005267 | -0.013457 | -0.001696 | 0.027112 | -0.013457 | -0.025753 | 0.017790 | -0.011581 | -0.026692 | -0.029939 | -0.025419 | 0.041343 | -0.020357 | -0.001576 | -0.004639 | 0.003940 | -0.111930 | -0.082276 | 0.030424 | 0.090118 | 0.095391 | 0.097681 | 0.106954 | 0.103425 | 0.005127 | 0.001962 | -0.057839 | -0.021739 | -0.029128 | 0.082181 | -0.063470 | 0.005787 | -0.055859 | 0.079769 | 0.008240 | 0.079157 | -0.023566 | 0.078283 | 0.053690 | -0.053261 | -0.065407 | -0.005763 | -0.014135 | 0.037538 | 0.031694 | -0.057463 | 0.035339 | 0.007374 | 0.015882 | -0.020346 | 0.010037 | -0.049561 | -0.030151 | 0.010744 | -0.073509 | 0.008114 | -0.053982 | 0.018575 | 0.015846 | 0.024428 | 0.032047 | -0.000404 | 0.018612 | 0.019680 | 0.020472 | -0.000963 | -0.023019 | -0.040206 | -0.022576 | 0.040737 | 0.011758 | 0.012124 | 0.002471 | 0.016025 | 0.025498 | -0.016739 | -0.023816 | 0.054414 | 0.022635 | 0.028223 | 0.045573 | -0.043270 | -0.021827 | -0.017368 | -0.050239 | 0.007093 | 0.032872 | -0.005806 | 0.030878 | 0.043150 | 0.154315 | 0.057170 | 0.037611 | 0.022366 | 0.041130 | -0.008533 | -0.013457 | 0.004604 | 0.029309 | -0.010628 | -0.011162 | 0.017770 | 0.009173 | 0.021364 | 0.000024 | 0.002992 | -0.006287 | 0.000562 | -0.001890 | 0.002292 | -0.112006 | -0.084904 | 0.017263 | 0.099091 | -0.002688 | 0.010486 | -0.003633 | -0.007141 | -0.069613 | -0.022313 | -0.034201 | 0.092698 | -0.063777 | 0.001075 | -0.055697 | 0.080794 | 0.006311 | 0.080006 | -0.019039 | 0.067809 | 0.026275 | -0.017541 | -0.015736 | -0.021801 | 0.015384 | -0.027942 | -0.013146 | 0.017885 | 0.134719 | 0.154726 | 0.016743 | 0.130843 | 0.155379 | 0.011270 | 0.137947 | 0.155918 | 0.065626 | 0.064739 | 0.074930 | 0.056512 | -0.023627 | -0.037068 | -0.002420 | -0.050267 | -0.002955 | -0.049474 | -0.000195 | -0.042226 | -0.027309 | -0.001152 | 0.079594 | 0.076481 | 0.078009 | 0.077502 | 0.079593 | 0.077307 | 0.013451 | -0.006830 | -0.005371 | -0.006785 | -0.002483 | -0.039036 | -0.043114 | -0.032164 |
| 19 | -0.054953 | 0.044733 | -0.014190 | 0.027088 | 0.025231 | -0.042985 | -0.028095 | 0.038832 | -0.009735 | -0.003256 | 0.016519 | 0.017281 | -0.002850 | -0.010012 | 0.000356 | 0.027317 | 0.020122 | -0.044985 | -0.029602 | 0.005644 | 0.005629 | -0.012171 | -0.041731 | 0.027035 | 0.017478 | 0.024712 | -0.042201 | 0.033903 | 0.048855 | -0.035492 | 0.038452 | 0.030422 | 0.055500 | 0.058095 | -0.055501 | 0.038590 | 0.023272 | 0.043062 | -0.011338 | 0.037542 | 0.034690 | 0.031840 | -0.077302 | -0.045870 | 0.090964 | 0.080200 | -0.003202 | 0.036504 | -0.029860 | -0.032713 | 0.060727 | -0.010956 | 0.010544 | 0.070983 | 0.031655 | 0.011869 | 0.057542 | -0.061673 | 0.042315 | -0.015407 | -0.000815 | -0.049581 | -0.011540 | 0.023804 | -0.037540 | 0.007683 | 0.023392 | -0.051670 | -0.031987 | -0.028413 | 0.028423 | 0.028229 | 0.027355 | -0.009718 | -0.015580 | -0.010833 | -0.010728 | -0.012420 | 0.020547 | -0.002383 | -0.009198 | -0.016746 | 0.031013 | 0.014808 | -0.050412 | -0.163664 | 0.029678 | 0.167019 | -0.160252 | -0.007479 | -0.018422 | -0.150466 | -0.014612 | 0.044478 | 0.005153 | -0.015422 | 0.049950 | -0.002427 | 0.003816 | 0.003848 | -0.007435 | 0.004266 | -0.022821 | -0.063181 | 0.041210 | 0.008730 | -0.008013 | 0.013135 | -0.001105 | 0.069994 | 0.017768 | 0.007915 | 0.071189 | -0.067141 | 0.050682 | -0.076018 | -0.055296 | -0.086341 | -0.055455 | -0.065627 | -0.012265 | 0.002079 | 0.056848 | -0.127076 | -0.116849 | -0.034316 | 0.009363 | 0.025210 | 0.029606 | -0.070309 | 0.019146 | 0.045335 | 0.081237 | 0.000890 | 0.005606 | -0.013141 | -0.009177 | 0.004095 | -0.009064 | 0.010469 | 0.052288 | 0.013865 | 0.002035 | 0.011387 | 0.020345 | -0.002107 | -0.005161 | -0.007218 | -0.014596 | 0.035408 | 0.009499 | 0.019956 | 0.020011 | 0.016710 | 0.029478 | 0.041751 | -0.001915 | 0.041753 | 0.014267 | -0.071240 | 0.073737 | 0.012933 | 0.028859 | 0.020406 | -0.080381 | 0.052452 | -0.001018 | -0.035193 | 0.076145 | -0.036099 | 0.002447 | -0.006105 | -0.006553 | -0.009092 | -0.015865 | 0.007473 | 0.010312 | -0.025094 | -0.003428 | -0.000767 | 0.023392 | -0.015501 | -0.019197 | 0.023392 | 0.023554 | 0.036986 | -0.005284 | -0.043478 | 0.002465 | 0.003685 | -0.001593 | -0.047412 | 0.017314 | 0.026701 | 0.042277 | -0.024394 | -0.002577 | -0.022258 | -0.081981 | 0.184282 | 0.213924 | 0.026185 | 0.017861 | 0.053275 | -0.006725 | 0.004683 | -0.082684 | 0.062520 | 0.046773 | 0.027773 | -0.006245 | -0.028851 | -0.086475 | 0.023863 | -0.006204 | 0.061322 | -0.127843 | -0.118349 | -0.039985 | 0.011034 | 0.025213 | 0.031380 | -0.077832 | 0.018160 | 0.037543 | 0.080583 | 0.001367 | 0.005811 | -0.009119 | -0.005692 | 0.004121 | -0.007282 | 0.011701 | 0.050259 | 0.011982 | -0.000218 | 0.011059 | 0.020674 | -0.004670 | -0.006014 | -0.007594 | -0.014289 | 0.033564 | 0.005642 | 0.014637 | 0.024578 | 0.012109 | 0.029182 | 0.043316 | -0.005153 | 0.043337 | 0.014125 | -0.074071 | 0.072390 | 0.002215 | 0.024910 | 0.024741 | -0.082205 | 0.052523 | -0.000446 | -0.039356 | 0.073694 | -0.053756 | 0.001073 | -0.010540 | -0.009576 | -0.006815 | -0.012080 | 0.000940 | 0.010007 | -0.018677 | -0.008971 | -0.004050 | 0.023392 | -0.018450 | -0.017273 | 0.023392 | 0.023076 | 0.034037 | -0.004651 | -0.043540 | 0.003431 | -0.002341 | 0.000986 | -0.049086 | 0.016290 | 0.024623 | 0.041432 | -0.024306 | -0.007153 | -0.023320 | -0.073778 | 0.186591 | 0.211499 | 0.215660 | 0.221987 | 0.033737 | 0.020459 | 0.047115 | -0.005739 | 0.004670 | -0.077770 | 0.062096 | 0.046793 | 0.026649 | -0.006914 | -0.028891 | -0.086776 | 0.028877 | -0.009568 | 0.057223 | -0.124139 | -0.118841 | -0.034521 | -0.001939 | 0.022885 | 0.033636 | -0.070423 | 0.015032 | 0.002230 | -0.011556 | 0.000254 | 0.006172 | -0.014730 | -0.008963 | 0.009130 | -0.012072 | 0.009543 | 0.051316 | 0.013429 | -0.005547 | -0.002109 | 0.016800 | 0.010962 | -0.009278 | -0.009198 | -0.010743 | 0.040892 | -0.011723 | -0.010884 | 0.022623 | 0.012886 | 0.023422 | 0.037792 | -0.008315 | 0.047237 | 0.013212 | -0.071310 | 0.072439 | 0.009748 | 0.027354 | 0.031160 | -0.076718 | 0.037212 | -0.004998 | -0.035543 | 0.064405 | -0.038193 | -0.018465 | -0.007152 | -0.006704 | -0.008342 | -0.071713 | 0.012491 | 0.010227 | -0.020853 | -0.022448 | -0.000897 | 0.023392 | -0.017095 | -0.026754 | 0.031110 | -0.031800 | -0.000262 | -0.029870 | -0.002586 | -0.009209 | 0.011784 | 0.000820 | 0.016818 | 0.027091 | 0.042984 | -0.024109 | -0.003057 | -0.025684 | -0.078853 | 0.010111 | -0.013615 | 0.052893 | 0.014328 | 0.053219 | -0.006551 | 0.011456 | -0.078532 | 0.062123 | 0.051660 | 0.027294 | -0.005935 | -0.029243 | -0.084789 | 0.024053 | -0.006259 | 0.007604 | 0.015905 | 0.012333 | 0.014946 | -0.047255 | -0.063403 | -0.007888 | -0.081268 | 0.049740 | 0.049299 | -0.084016 | 0.052628 | 0.050484 | -0.083973 | 0.053323 | 0.049844 | -0.027725 | -0.042228 | -0.035531 | -0.040266 | -0.006459 | 0.029182 | 0.011372 | 0.048359 | 0.011475 | 0.049170 | 0.011481 | 0.044136 | 0.043663 | 0.016960 | -0.050958 | -0.035984 | -0.053407 | -0.044953 | -0.053084 | -0.042581 | -0.012020 | 0.029695 | 0.029968 | 0.029754 | -0.026144 | -0.015481 | -0.012984 | 0.017531 |
| 20 | 0.014525 | 0.007542 | -0.007199 | 0.054171 | -0.002732 | -0.061618 | 0.062232 | -0.016150 | 0.007728 | -0.004276 | 0.001579 | 0.001454 | -0.017372 | 0.007934 | 0.003307 | -0.016941 | -0.000967 | 0.178064 | -0.001193 | -0.013215 | 0.000668 | -0.016970 | 0.000390 | -0.011794 | -0.001728 | -0.015054 | -0.016499 | 0.051918 | 0.046473 | 0.017244 | -0.004249 | 0.001386 | -0.006448 | -0.003056 | 0.006449 | 0.009226 | 0.017150 | -0.036891 | -0.018107 | -0.008314 | 0.028199 | 0.012833 | 0.010163 | 0.031202 | 0.024865 | 0.008758 | 0.034162 | 0.031484 | 0.036846 | 0.049133 | 0.021534 | -0.016781 | -0.037000 | -0.047232 | -0.026462 | 0.035248 | 0.016185 | -0.007810 | 0.032894 | 0.026965 | 0.009163 | 0.028392 | -0.022267 | -0.002286 | 0.014507 | 0.022066 | 0.019030 | -0.021800 | -0.011572 | -0.015625 | 0.027040 | -0.047237 | 0.042802 | 0.047079 | 0.034286 | -0.018428 | 0.013650 | 0.017305 | 0.062772 | -0.011546 | -0.027497 | 0.014059 | 0.012576 | -0.024509 | 0.043651 | 0.027191 | 0.010372 | -0.028055 | 0.029250 | -0.050588 | 0.004187 | 0.023439 | -0.016201 | -0.027338 | 0.049063 | 0.021663 | -0.038121 | -0.005721 | 0.031206 | -0.028136 | -0.020998 | 0.045708 | 0.007275 | -0.096772 | 0.222672 | -0.029779 | 0.001647 | -0.001931 | 0.068567 | -0.028135 | 0.063646 | 0.044013 | -0.003666 | 0.010293 | -0.043640 | -0.032093 | -0.037322 | 0.028317 | 0.000369 | -0.020917 | 0.039904 | 0.030565 | 0.035515 | 0.040460 | 0.012042 | -0.033384 | 0.063058 | -0.002809 | 0.022693 | -0.013204 | -0.024153 | -0.041856 | -0.080923 | -0.004843 | -0.000397 | 0.005856 | 0.038938 | 0.001559 | -0.014056 | -0.004138 | -0.202894 | 0.030394 | -0.027184 | -0.028120 | -0.015185 | -0.012300 | -0.011821 | -0.009677 | -0.008865 | -0.016418 | -0.068646 | -0.053933 | -0.020687 | 0.064111 | -0.034364 | -0.042166 | -0.030894 | -0.042177 | 0.066386 | 0.010489 | -0.000165 | -0.008828 | -0.012829 | 0.032216 | 0.000645 | 0.029154 | 0.013276 | 0.041384 | 0.010028 | 0.077337 | -0.003786 | -0.024777 | -0.029688 | 0.001317 | -0.030876 | 0.007375 | -0.000570 | -0.015625 | -0.017034 | -0.029342 | 0.019030 | -0.008510 | -0.040069 | 0.019030 | -0.045787 | 0.036061 | -0.019753 | 0.057607 | -0.052996 | -0.092141 | 0.041835 | 0.059139 | 0.025236 | 0.014954 | 0.068172 | -0.112752 | -0.074164 | -0.024644 | -0.051710 | 0.001895 | -0.011606 | 0.008341 | -0.062601 | -0.019481 | 0.030841 | 0.045913 | -0.049954 | 0.141532 | 0.231539 | 0.072503 | -0.029022 | -0.104786 | -0.046540 | 0.073087 | 0.012918 | 0.031329 | 0.039654 | 0.013058 | -0.028710 | 0.063533 | -0.002812 | 0.022064 | -0.013085 | -0.023452 | -0.039157 | -0.081033 | -0.005444 | -0.001070 | 0.006885 | 0.039400 | 0.001572 | -0.013980 | -0.006770 | -0.197960 | 0.029426 | -0.025499 | -0.027178 | -0.016445 | -0.014168 | -0.012036 | -0.009503 | -0.008625 | -0.019364 | -0.065972 | -0.049615 | -0.023198 | 0.059703 | -0.030775 | -0.034658 | -0.025866 | -0.034645 | 0.063510 | 0.008450 | -0.000886 | -0.010499 | -0.020643 | 0.032097 | -0.001701 | 0.026046 | 0.013120 | 0.043413 | 0.016340 | 0.067457 | -0.000353 | -0.027897 | -0.030649 | 0.003879 | -0.001762 | -0.005854 | 0.001165 | -0.005364 | -0.023306 | -0.030469 | 0.019030 | -0.004532 | -0.040682 | 0.019030 | -0.049376 | 0.034523 | -0.020907 | 0.057500 | -0.057211 | -0.092328 | 0.049488 | 0.058415 | 0.026771 | 0.012108 | 0.066311 | -0.111933 | -0.071899 | -0.024113 | -0.046685 | 0.004431 | -0.009207 | -0.004113 | 0.001670 | 0.008231 | -0.057399 | -0.011274 | 0.031179 | 0.045983 | -0.045443 | 0.141251 | 0.231548 | 0.071869 | -0.027408 | -0.105251 | -0.047092 | 0.080336 | 0.009843 | 0.035042 | 0.039644 | 0.010920 | -0.033431 | 0.048244 | 0.005866 | 0.026332 | -0.015808 | -0.023898 | -0.021092 | 0.030119 | -0.003777 | -0.001072 | 0.023956 | 0.039474 | 0.001890 | -0.010974 | -0.003473 | -0.203333 | 0.029744 | -0.016864 | -0.017828 | -0.000034 | 0.008889 | -0.006519 | -0.006676 | -0.005832 | -0.015460 | -0.076598 | -0.071295 | -0.020503 | 0.065169 | -0.033770 | -0.041811 | -0.029047 | -0.042831 | 0.065968 | 0.010238 | 0.000133 | -0.012040 | -0.014096 | 0.031321 | -0.001331 | 0.023868 | 0.009759 | 0.040319 | -0.017437 | 0.077041 | -0.002124 | -0.024947 | -0.028003 | 0.003028 | -0.033645 | 0.002892 | 0.012899 | -0.015319 | -0.044560 | -0.025092 | 0.019030 | -0.011614 | -0.045248 | 0.008409 | -0.020328 | -0.004156 | 0.011335 | -0.006064 | -0.016631 | 0.038660 | -0.002178 | 0.026070 | 0.013614 | 0.067131 | -0.113158 | -0.073793 | -0.015649 | -0.052680 | -0.007845 | -0.011712 | -0.011729 | -0.021364 | -0.013114 | 0.030853 | 0.034468 | -0.052726 | 0.139999 | 0.234590 | 0.072584 | -0.028722 | -0.105521 | -0.047331 | 0.072263 | 0.005961 | 0.039387 | -0.017498 | -0.031153 | -0.023767 | 0.010883 | 0.011698 | 0.009694 | 0.005929 | -0.020627 | -0.012573 | 0.008518 | -0.023821 | -0.012467 | 0.002700 | -0.020936 | -0.012948 | 0.000869 | 0.033949 | 0.026221 | 0.036307 | 0.026435 | -0.060841 | -0.048141 | -0.095695 | -0.043512 | -0.096079 | -0.050172 | -0.086169 | -0.028680 | -0.022206 | 0.057035 | 0.048324 | 0.058612 | 0.052490 | 0.057207 | 0.055875 | 0.025797 | 0.013292 | 0.012255 | 0.013236 | 0.015492 | 0.014426 | 0.009982 | -0.002786 |
| 21 | -0.003653 | -0.025261 | 0.006462 | 0.044790 | 0.021146 | -0.042684 | -0.020886 | -0.026534 | 0.014264 | -0.001348 | -0.001870 | 0.009342 | -0.025141 | 0.001398 | -0.002517 | -0.018286 | 0.013235 | -0.010124 | 0.014725 | -0.038585 | 0.029778 | 0.014376 | 0.052945 | -0.062201 | -0.035027 | -0.060197 | 0.024280 | -0.045055 | -0.065630 | 0.069755 | -0.001581 | 0.019145 | -0.006055 | 0.002108 | 0.006055 | 0.048794 | 0.010793 | 0.027582 | 0.017687 | -0.016437 | 0.016042 | 0.028860 | -0.061600 | -0.047102 | -0.033698 | -0.011032 | -0.050137 | -0.057838 | -0.020381 | -0.029579 | 0.002517 | -0.019015 | 0.026498 | -0.017150 | 0.022719 | 0.006594 | 0.042793 | -0.025892 | -0.030819 | -0.054468 | -0.061894 | -0.016911 | -0.009246 | 0.012922 | -0.013228 | -0.005547 | 0.025540 | 0.003001 | -0.024393 | 0.019290 | -0.009490 | -0.004177 | -0.042102 | 0.039043 | 0.001774 | 0.050186 | -0.035208 | 0.003988 | -0.163495 | 0.006130 | 0.021419 | -0.015523 | -0.001570 | 0.009923 | -0.009201 | 0.022507 | -0.014725 | -0.019916 | 0.020677 | 0.014101 | 0.002727 | 0.020266 | 0.003380 | -0.042832 | -0.016377 | -0.009507 | 0.006839 | 0.022933 | -0.015400 | 0.010273 | 0.008018 | -0.040496 | -0.006869 | -0.069779 | 0.225052 | 0.025237 | -0.096389 | 0.033730 | 0.008903 | -0.008849 | 0.000233 | 0.012355 | 0.007668 | -0.001027 | -0.009362 | 0.004265 | 0.015738 | 0.011298 | 0.020236 | 0.020397 | 0.000249 | 0.007666 | -0.015881 | 0.030575 | 0.035993 | -0.060195 | -0.030504 | 0.021063 | 0.015303 | -0.052551 | -0.029029 | -0.009111 | 0.016290 | -0.003093 | 0.000846 | -0.055056 | 0.021815 | 0.002630 | -0.002732 | -0.004047 | 0.021713 | -0.025659 | -0.007941 | 0.023529 | 0.030699 | -0.015386 | 0.000230 | 0.005546 | 0.005968 | 0.164841 | 0.103021 | 0.060258 | -0.055463 | -0.054253 | 0.028812 | -0.012440 | 0.017013 | -0.012445 | -0.021246 | -0.018743 | 0.032126 | 0.082467 | 0.027345 | -0.002811 | -0.023545 | -0.017469 | -0.014124 | -0.022132 | -0.046985 | -0.080364 | -0.005477 | 0.003675 | -0.034449 | -0.007337 | -0.017183 | -0.012588 | 0.005192 | 0.005565 | -0.000683 | 0.001005 | 0.025540 | 0.004397 | 0.001313 | 0.025540 | 0.032556 | 0.010783 | 0.012176 | 0.029319 | 0.038627 | 0.006962 | 0.011161 | 0.018344 | -0.065653 | -0.064621 | -0.030999 | 0.208274 | 0.177830 | 0.000101 | 0.025540 | -0.027586 | 0.008712 | -0.027080 | 0.005305 | -0.026681 | -0.039429 | -0.042233 | 0.024455 | 0.119426 | 0.228902 | -0.053103 | 0.094271 | -0.000728 | -0.002374 | 0.013205 | 0.005223 | -0.010700 | 0.028668 | 0.032610 | -0.062220 | -0.024343 | 0.021064 | 0.012924 | -0.051260 | -0.029192 | -0.010504 | 0.012830 | -0.003025 | 0.001937 | -0.056524 | 0.024594 | 0.002606 | -0.002354 | -0.000702 | 0.025620 | -0.026322 | -0.006791 | 0.021409 | 0.030667 | -0.011049 | -0.000949 | 0.005258 | 0.005942 | 0.159149 | 0.100589 | 0.058906 | -0.056585 | -0.043998 | 0.026440 | -0.016817 | 0.021129 | -0.016818 | -0.029002 | -0.021872 | 0.035366 | 0.076962 | 0.035169 | -0.005273 | -0.024873 | -0.019004 | -0.014739 | -0.023696 | -0.049824 | -0.074712 | -0.001930 | 0.000419 | -0.033466 | -0.004752 | -0.008317 | -0.015712 | 0.008859 | 0.009912 | -0.006464 | -0.000914 | 0.025540 | 0.002023 | 0.000396 | 0.025540 | 0.033463 | 0.010497 | 0.009762 | 0.027973 | 0.039632 | 0.007204 | 0.007835 | 0.014776 | -0.057059 | -0.061974 | -0.030877 | 0.204462 | 0.175463 | -0.000449 | 0.029036 | -0.024008 | 0.008184 | -0.010743 | -0.013202 | -0.024256 | 0.000295 | -0.032524 | -0.039221 | -0.042246 | 0.022244 | 0.119323 | 0.228833 | -0.056543 | 0.092400 | 0.002010 | -0.003992 | 0.015612 | 0.011803 | -0.016313 | 0.030461 | 0.037037 | -0.061652 | -0.058450 | 0.005629 | 0.021150 | -0.053466 | -0.026428 | 0.000097 | -0.003189 | -0.002292 | 0.000030 | -0.038650 | 0.022715 | 0.000841 | -0.001142 | -0.003708 | 0.021633 | -0.025176 | 0.001336 | 0.017237 | 0.004086 | -0.055664 | 0.010722 | 0.009958 | 0.010038 | 0.161541 | 0.076353 | 0.095164 | -0.058218 | -0.053754 | 0.025116 | -0.011114 | 0.016158 | -0.013759 | -0.022870 | -0.019016 | 0.032086 | 0.082061 | 0.026623 | 0.005785 | -0.020104 | -0.009028 | -0.010859 | -0.020284 | 0.000725 | -0.079564 | -0.004741 | 0.002981 | -0.035906 | -0.010196 | 0.019725 | 0.002264 | 0.012390 | 0.007184 | -0.012787 | -0.000264 | 0.025540 | 0.003510 | 0.007669 | -0.002954 | 0.009394 | -0.000875 | 0.001410 | -0.028174 | -0.027802 | 0.024982 | -0.008763 | -0.068524 | -0.061696 | -0.031008 | 0.206791 | 0.177937 | -0.016315 | 0.027402 | -0.017914 | 0.029109 | -0.055528 | 0.009890 | -0.032500 | -0.039818 | -0.023106 | 0.028668 | 0.117906 | 0.232236 | -0.053352 | 0.095379 | -0.003060 | -0.003573 | 0.013233 | 0.003353 | -0.032446 | -0.037125 | -0.033697 | -0.033872 | 0.061979 | 0.015850 | -0.000811 | 0.072863 | 0.033932 | 0.026260 | 0.071088 | 0.032148 | 0.025656 | 0.062330 | 0.033164 | 0.026510 | 0.009735 | -0.007166 | -0.005138 | -0.010104 | 0.007280 | -0.023858 | -0.009537 | -0.010250 | -0.010318 | -0.010614 | -0.009672 | -0.003541 | -0.001453 | 0.003417 | -0.019291 | -0.012657 | -0.018029 | -0.013374 | -0.018095 | -0.017941 | 0.017568 | -0.038861 | -0.040441 | -0.038995 | -0.006806 | 0.007732 | 0.010359 | 0.008785 |
| 22 | 0.044439 | -0.010362 | 0.016004 | 0.026375 | 0.009738 | -0.007457 | -0.000907 | 0.004521 | -0.001786 | -0.000461 | 0.065544 | 0.018068 | -0.001078 | -0.000268 | 0.011691 | 0.049726 | 0.011242 | 0.006609 | 0.031911 | -0.032672 | 0.004032 | 0.009143 | 0.034253 | -0.041542 | -0.026172 | -0.048566 | 0.018025 | -0.004146 | -0.020488 | 0.041152 | -0.018888 | -0.002323 | -0.037553 | -0.027760 | 0.037554 | 0.009759 | -0.023081 | -0.040773 | 0.016041 | -0.032738 | -0.039190 | -0.029339 | 0.140453 | 0.112598 | 0.065280 | 0.007949 | 0.103408 | 0.126540 | -0.001061 | 0.008181 | -0.039257 | -0.015206 | -0.025730 | -0.063518 | -0.087851 | -0.004562 | -0.085315 | 0.070098 | 0.037753 | 0.089878 | 0.089168 | 0.066911 | -0.007999 | -0.012146 | 0.055045 | 0.032531 | -0.017524 | 0.053068 | -0.002762 | 0.030061 | 0.055575 | -0.034547 | 0.011798 | -0.002466 | 0.048263 | -0.019205 | 0.006724 | -0.012696 | -0.043256 | -0.069636 | -0.017151 | 0.006685 | -0.040726 | -0.047408 | 0.025772 | -0.040210 | -0.000291 | 0.039908 | -0.022949 | -0.019481 | -0.037646 | -0.018744 | 0.011766 | -0.050580 | 0.020577 | 0.046478 | -0.027857 | -0.001990 | -0.010114 | -0.039372 | -0.030181 | 0.030157 | -0.022620 | 0.052367 | -0.029945 | -0.025795 | -0.071197 | 0.038607 | -0.031172 | -0.124789 | -0.059638 | -0.056646 | -0.076581 | 0.054791 | -0.118994 | 0.062650 | -0.036725 | 0.127658 | 0.088309 | 0.106835 | -0.013326 | -0.019348 | -0.028969 | 0.047632 | 0.036254 | -0.043149 | -0.043141 | 0.009744 | -0.005234 | -0.002050 | 0.001032 | -0.059277 | -0.042097 | -0.013762 | 0.022004 | 0.004792 | 0.015363 | 0.022986 | -0.073747 | 0.018076 | 0.012194 | -0.016849 | -0.001092 | -0.008201 | -0.012499 | -0.046551 | -0.010101 | 0.000181 | 0.000647 | 0.053136 | 0.005919 | -0.029530 | -0.032400 | -0.024796 | -0.034100 | -0.046597 | -0.021413 | -0.046609 | 0.044437 | -0.011116 | 0.004513 | -0.058221 | -0.090355 | 0.010047 | 0.082343 | 0.036417 | 0.018538 | 0.065989 | 0.119361 | 0.033232 | -0.004995 | -0.020919 | 0.022806 | 0.009253 | 0.030616 | 0.021150 | -0.004049 | 0.009373 | -0.013908 | -0.019628 | -0.017524 | 0.000847 | -0.017727 | -0.017524 | -0.006448 | -0.039663 | 0.054597 | 0.015850 | -0.028176 | -0.026432 | -0.001341 | 0.032168 | 0.054297 | -0.028557 | 0.048845 | 0.089030 | 0.077161 | -0.014473 | -0.094284 | 0.147776 | 0.148123 | 0.002448 | 0.021090 | -0.063223 | 0.020884 | 0.031307 | -0.096045 | -0.046512 | -0.033318 | -0.031794 | 0.067222 | -0.037638 | 0.097615 | -0.026960 | 0.091928 | -0.013999 | 0.050172 | 0.035492 | -0.043031 | -0.039885 | 0.009740 | -0.002402 | 0.001128 | -0.001940 | -0.062272 | -0.047142 | -0.014083 | 0.021784 | 0.004106 | 0.012042 | 0.022993 | -0.075046 | 0.016854 | 0.006566 | -0.015659 | -0.000756 | -0.008729 | -0.012633 | -0.047919 | -0.008922 | -0.000769 | -0.000172 | 0.050957 | 0.007035 | -0.030978 | -0.034622 | -0.024455 | -0.031673 | -0.041646 | -0.023668 | -0.041648 | 0.039311 | -0.010552 | 0.004011 | -0.064882 | -0.100418 | 0.009493 | 0.081307 | 0.034992 | 0.018664 | 0.070453 | 0.118043 | 0.045273 | -0.008993 | -0.018897 | 0.020629 | 0.002554 | 0.005680 | 0.018332 | -0.009356 | 0.007224 | -0.011162 | -0.016890 | -0.017524 | 0.004272 | -0.010249 | -0.017524 | -0.013111 | -0.040919 | 0.054778 | 0.017908 | -0.026218 | -0.023960 | -0.007698 | 0.033074 | 0.056826 | -0.029221 | 0.047522 | 0.088016 | 0.076202 | -0.013749 | -0.085402 | 0.150151 | 0.151439 | 0.147459 | 0.139922 | 0.004924 | 0.027082 | -0.038008 | 0.021458 | 0.031307 | -0.088560 | -0.046334 | -0.033257 | -0.029738 | 0.068722 | -0.031885 | 0.095835 | -0.029612 | 0.088650 | -0.029528 | 0.044546 | 0.036980 | -0.043885 | -0.058388 | 0.003777 | -0.002003 | -0.003362 | -0.000145 | 0.017405 | 0.020476 | -0.011860 | 0.021401 | -0.000420 | 0.016548 | 0.023167 | -0.073965 | 0.018305 | 0.012156 | -0.017801 | -0.000808 | -0.004128 | -0.017241 | -0.018549 | -0.000302 | 0.001605 | 0.002244 | 0.049734 | -0.005112 | 0.000113 | -0.033934 | -0.023205 | -0.034689 | -0.044236 | -0.016668 | -0.049909 | 0.044004 | -0.010816 | 0.004989 | -0.054982 | -0.089980 | -0.009679 | 0.074143 | 0.018866 | 0.014608 | 0.063003 | 0.000133 | 0.034065 | 0.032705 | -0.020491 | 0.026569 | 0.014276 | -0.003579 | -0.006267 | -0.011659 | 0.006006 | 0.014579 | -0.016241 | -0.017524 | 0.001384 | -0.025016 | 0.010572 | -0.028154 | 0.005167 | 0.055946 | 0.004391 | 0.016417 | -0.011354 | 0.043279 | 0.052687 | -0.028859 | 0.049267 | 0.088899 | 0.082047 | -0.000113 | -0.095102 | -0.021845 | -0.020725 | -0.022578 | -0.004282 | -0.053230 | 0.021271 | 0.031349 | -0.093530 | -0.046114 | -0.036952 | -0.030871 | 0.067623 | -0.040164 | 0.092832 | -0.026502 | 0.097523 | 0.029018 | 0.014511 | -0.017789 | 0.011149 | 0.050483 | 0.014896 | -0.011042 | 0.062466 | -0.025328 | -0.002086 | 0.061853 | -0.028741 | -0.004436 | 0.050155 | -0.025591 | -0.001810 | 0.020492 | 0.035529 | 0.034551 | 0.034196 | -0.038405 | 0.052296 | 0.118777 | 0.112170 | 0.117384 | 0.113794 | 0.121314 | 0.111259 | 0.013537 | -0.002504 | 0.010160 | 0.018827 | 0.011001 | 0.018515 | 0.008187 | 0.019551 | -0.012740 | 0.082654 | 0.081071 | 0.082646 | 0.035359 | 0.012446 | 0.009429 | -0.024639 |
print('''\n\033[1m''' + '''Variance Percentage of selected component''' + '''\033[0m''')
pca2.explained_variance_
Variance Percentage of selected component
array([25.56358838, 17.21005398, 13.33863878, 11.94664722, 9.78900909,
9.279333 , 8.61368173, 8.47403682, 7.61176772, 6.86545467,
6.26965256, 6.16216624, 5.99758684, 5.92749399, 5.62402098,
5.37670994, 5.33355564, 5.16471791, 4.9672043 , 4.80401532,
4.71908118, 4.60170061, 4.46807018])
print('''\n\033[1m''' + '''Total sum variance Percentage of selected component''' + '''\033[0m''')
sum(pca2.explained_variance_ratio_*100)
Total sum variance Percentage of selected component
42.14980797242
print('''\n\033[1m''' + '''Graph Visualization of selected components''' + '''\033[0m''')
plt.figure(figsize=(10 , 3))
plt.bar(range(1, n+1), pca2.explained_variance_ratio_, label = 'Individual explained variance',color='lightblue',edgecolor='black')
plt.step(range(1, n+1), np.cumsum(pca2.explained_variance_ratio_),where='mid', label = 'Cumulative explained variance',color = 'black')
plt.ylabel('Explained Variance Ratio') # x axis label
plt.xlabel('Principal Components') # y axis label
plt.legend()
Graph Visualization of selected components
<matplotlib.legend.Legend at 0x11cc52670>
pca_transformed = pca2.transform(x)
print('''\n\033[1m''' + '''Spliting dataset''' + '''\033[0m''')
px_train, px_test, py_train, py_test = train_test_split(pca_transformed, y, test_size = 0.3, random_state = 202)
print(' shape of pca_x_train:',px_train.shape)
print(' shape of pca_x_test:',px_test.shape)
Spliting dataset
shape of pca_x_train: (1096, 23)
shape of pca_x_test: (471, 23)
y[y < 0] = -1
y[y > 0] = 1
y.value_counts()
-1.0 1463 1.0 104 Name: Pass/Fail, dtype: int64
# RandomOverSampler
from imblearn.over_sampling import RandomOverSampler
balpc = RandomOverSampler()
xupp, yupp= balpc.fit_sample(x, y)
rn=xupp.shape[0] - x.shape[0]
print('new random picked points')
yupp.value_counts().plot(kind='bar', title='Count (target)');
rn
new random picked points
1359
nsdp = pd.concat([xupp,yupp], axis=1)
nsdp.head(5)
| 0 | 1 | 2 | 3 | 4 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 43 | 44 | 45 | 46 | 47 | 48 | 50 | 51 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 70 | 71 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 180 | 181 | 182 | 183 | 184 | 185 | 187 | 188 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 221 | 222 | 223 | 224 | 225 | 227 | 228 | 238 | 239 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 316 | 317 | 318 | 319 | 320 | 321 | 323 | 324 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 347 | 348 | 349 | 350 | 351 | 352 | 353 | 354 | 355 | 356 | 357 | 359 | 360 | 361 | 362 | 363 | 365 | 366 | 367 | 368 | 376 | 377 | 385 | 386 | 387 | 388 | 389 | 390 | 391 | 392 | 393 | 405 | 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | 415 | 416 | 417 | 418 | 419 | 420 | 421 | 423 | 424 | 425 | 426 | 427 | 428 | 429 | 430 | 431 | 432 | 433 | 434 | 435 | 436 | 437 | 438 | 439 | 440 | 441 | 442 | 443 | 444 | 445 | 446 | 447 | 448 | 452 | 453 | 454 | 455 | 456 | 457 | 459 | 460 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 477 | 478 | 479 | 480 | 482 | 483 | 484 | 485 | 486 | 487 | 488 | 489 | 490 | 491 | 493 | 494 | 495 | 496 | 497 | 499 | 500 | 510 | 511 | 519 | 520 | 521 | 522 | 523 | 524 | 525 | 526 | 527 | 539 | 540 | 541 | 542 | 543 | 544 | 545 | 546 | 547 | 548 | 549 | 550 | 551 | 552 | 553 | 554 | 555 | 556 | 557 | 558 | 559 | 560 | 561 | 562 | 563 | 564 | 565 | 566 | 567 | 568 | 569 | 570 | 571 | 572 | 573 | 574 | 575 | 576 | 577 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.224463 | 0.849523 | -0.436430 | 0.035804 | -0.050121 | -0.564354 | 0.265894 | 0.509848 | 1.128455 | -0.381577 | -1.515617 | 0.763117 | -0.375756 | 0.103879 | 0.056566 | -0.286055 | 0.845957 | 0.174249 | -0.146683 | 0.318209 | 0.735614 | -0.172197 | 0.361844 | -1.738184 | -0.874376 | -2.887917 | -1.522835 | -0.842542 | -0.636905 | -0.287505 | -0.958019 | 0.411164 | 0.029174 | -0.115322 | -0.029182 | 0.305382 | 0.266386 | -0.645694 | -0.283379 | 0.498003 | -0.452655 | 0.874271 | -0.812538 | -0.852081 | 0.681680 | 0.277780 | -0.919041 | 0.999838 | -0.017844 | 0.041453 | -0.862229 | 0.418572 | -0.195538 | 1.323374 | -0.491928 | -0.702824 | 0.728880 | -0.912747 | 0.303850 | 0.239003 | 0.339085 | -1.111942 | -0.051277 | 0.274832 | -1.148517 | -0.640985 | -0.02527 | 0.881524 | 0.266656 | 0.680780 | -0.359555 | -0.511608 | 0.323056 | -2.106521 | -0.292813 | -0.457000 | -0.220082 | -0.331499 | -1.044890 | -1.124999 | -0.090271 | -0.399141 | -3.770051 | -1.860871 | 0.146352 | 0.742635 | 2.304328 | -1.349572 | 0.974932 | -0.425465 | 0.623511 | 0.940718 | 2.131088 | 1.825649 | 0.370180 | 1.908082 | -0.180534 | -2.807550 | 3.816726 | 0.079873 | 0.047474 | -0.073477 | 0.025089 | 0.386589 | -0.030126 | 0.221393 | 1.075702 | 0.546711 | 0.842009 | -1.397272 | 1.021946 | 1.189792 | -1.141040 | 2.384047 | -1.768518 | -0.015944 | 0.414601 | -0.252273 | -0.805928 | -0.411341 | -0.508802 | -0.051032 | 0.088230 | -0.499742 | -0.910786 | -0.926805 | -0.249510 | -0.050451 | 0.040121 | -0.600920 | -0.492646 | 0.806501 | -0.879920 | 0.567994 | 0.202214 | -0.784953 | -0.121355 | -0.030939 | 0.511593 | 0.801234 | -0.085803 | -0.094613 | 0.137099 | 0.717086 | -0.708061 | -0.675808 | -0.411725 | -0.329719 | -0.385650 | -0.704959 | -0.531093 | -1.163815 | -1.792703 | 0.258431 | -0.101393 | -0.025491 | 0.131773 | -0.024089 | 1.478412 | 2.045992 | -0.830108 | 0.585265 | -0.953163 | 0.410881 | -1.456217 | -0.585753 | -0.229023 | -0.784044 | 1.020986 | 0.012640 | -0.210440 | 0.000955 | -0.486144 | -0.054889 | -0.841960 | -0.482042 | -0.419775 | -0.794549 | -0.056399 | -0.035179 | -0.02527 | -0.577007 | 0.858785 | -0.02527 | -0.276105 | 0.128966 | 0.597120 | -0.106060 | -0.227593 | -0.963695 | -2.089364 | 0.914821 | -0.250044 | -1.373707 | 0.428472 | 0.219582 | 0.949805 | -0.388463 | 2.116156 | 5.144168 | 3.424208 | -1.232734 | -0.399062 | -0.281132 | -0.129445 | -0.067565 | 2.020788 | -0.085018 | -0.028099 | -0.641834 | -0.979624 | 0.191627 | -0.791079 | 2.916146 | 0.642033 | 0.205434 | -0.678459 | -1.039246 | -1.107771 | -0.303274 | -0.050465 | 0.318029 | -0.346240 | -0.451498 | 0.257514 | -0.558185 | 0.600637 | 0.192585 | -0.631168 | -0.069446 | -0.031638 | 0.537776 | 0.784035 | -0.057288 | -0.126317 | 0.034575 | 0.513271 | -0.700430 | -0.676908 | -0.412851 | -0.306353 | -0.329499 | -0.909892 | -0.507350 | -1.403195 | -1.769648 | 0.310084 | -0.205669 | 0.192301 | 0.699549 | 0.195915 | 1.433257 | 1.589957 | -0.844610 | 0.287741 | -0.753640 | 0.520266 | -1.167579 | -0.555565 | -0.301756 | -0.853058 | 0.707864 | -0.126902 | -0.176208 | 0.110233 | -0.445849 | -0.057607 | -0.168241 | -0.429074 | -0.320769 | -0.658114 | -0.055641 | 0.033982 | -0.02527 | -0.597159 | 0.976545 | -0.02527 | -0.269615 | 0.205995 | 0.588231 | -0.153861 | -0.209531 | -1.076802 | -2.125201 | 0.661139 | -0.071898 | -1.198768 | 0.428934 | 0.177791 | 0.895409 | -0.360974 | 1.667683 | 4.447710 | 4.195657 | 3.203869 | 3.915044 | -1.125490 | -0.580658 | -0.232199 | -0.179601 | -0.068016 | 1.535180 | -0.087905 | -0.021416 | -0.410571 | -0.958773 | 0.252902 | -0.851226 | 2.662305 | 0.569658 | 0.108501 | -0.512152 | -0.942375 | -0.917673 | -0.328325 | 0.073534 | 0.081033 | -0.644611 | -0.544418 | 0.712732 | -0.950407 | 0.537868 | 0.272203 | -0.735988 | -0.132243 | -0.089474 | 0.496633 | 0.698334 | -0.088238 | -0.087582 | 0.009855 | 0.297172 | -0.578641 | -0.693567 | -0.137275 | -0.193877 | -0.127194 | -0.560884 | -0.298990 | -0.739794 | -1.658161 | 0.365113 | -0.224214 | -0.035879 | 0.136739 | -0.016240 | 1.462140 | 2.033503 | -0.830117 | 0.645357 | -0.974058 | 0.555004 | -1.414080 | -0.760863 | -0.267520 | -0.777840 | 0.055821 | -0.005236 | 0.381334 | 0.021031 | -0.518080 | 0.014282 | -1.557322 | -0.653556 | -0.966730 | -0.738187 | 0.796401 | -0.043280 | -0.02527 | -0.603717 | 1.127496 | 1.059160 | 0.448214 | 1.325827 | -0.099377 | 1.898791 | -0.905860 | -1.197654 | -1.199061 | -0.174260 | -1.380622 | 0.438514 | 0.184098 | 0.987510 | -0.515104 | 2.170517 | -0.809806 | -0.745562 | 0.236833 | -0.836511 | -0.266711 | -0.124479 | -0.112621 | 2.062967 | -0.083086 | -0.040267 | -0.620486 | -0.978015 | 0.157239 | -0.782303 | 2.932521 | 0.656685 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | 0.120805 | -1.709169 | 0.106900 | -0.336030 | -0.874533 | -0.351836 | -0.318719 | -0.959499 | -0.298562 | -0.538278 | -0.789264 | -0.338360 | 0.470186 | 0.560524 | 0.610426 | 0.531416 | 0.183094 | -0.212815 | -0.434630 | -0.289437 | -0.398435 | -0.314028 | -0.405707 | -0.362419 | 0.190142 | 0.034410 | -0.226018 | -0.120518 | -0.226665 | -0.031418 | -0.229797 | -0.135520 | 0.118679 | -0.204833 | -0.093165 | -0.197057 | -0.077554 | -0.190165 | -0.238334 | -0.295753 | -1.0 |
| 1 | 1.107287 | -0.383106 | 1.016977 | 0.155282 | -0.059585 | 0.197639 | 0.321868 | 0.457021 | 0.022620 | -1.608281 | -0.133006 | 0.181528 | 0.411562 | 0.095954 | -0.269742 | -0.111740 | 0.446647 | -0.093702 | -1.348004 | 0.282279 | -0.322078 | 0.223063 | -0.462748 | 0.526079 | 0.400176 | 0.603714 | -0.311431 | -0.243722 | 0.791123 | -0.478202 | -0.213375 | 0.624843 | 0.065219 | -0.105995 | -0.065226 | -4.283580 | 1.539930 | -0.310382 | 0.426118 | -0.245879 | -0.528535 | 0.035059 | -0.455129 | -0.727337 | 0.584067 | 1.298778 | -0.114815 | 0.783909 | -0.054299 | 0.058263 | -0.123172 | 0.521553 | -0.315462 | 1.044437 | -0.225470 | -0.523124 | -0.415640 | -0.292833 | -0.434113 | -0.271323 | 0.070691 | -0.816696 | -0.050504 | 1.638696 | 0.132044 | -0.695761 | -0.02527 | 0.135382 | 0.290945 | 0.240535 | -0.639619 | -1.712613 | 0.130617 | -0.981320 | -0.122354 | -1.255571 | 0.535338 | -0.709313 | 0.543432 | 2.313733 | -0.026190 | -1.078926 | 1.635323 | -0.621687 | 0.179608 | -2.710508 | 0.385687 | 2.594036 | -2.152325 | -0.276903 | -1.067849 | -1.783222 | -1.214612 | 1.727662 | 0.252518 | 0.624916 | -0.180534 | 0.904673 | -0.918512 | 0.079873 | -0.241090 | -0.073477 | -0.329673 | 0.323387 | 0.006493 | -0.326078 | 0.259668 | 1.571137 | 0.842009 | -1.506012 | 0.744088 | 0.750364 | -1.124234 | 0.080589 | -1.738883 | 0.303047 | 0.375734 | 0.816107 | 0.308458 | -0.477342 | -0.910434 | -0.492334 | -0.347032 | -1.076061 | -0.799505 | -0.112948 | -0.748877 | -0.050777 | -0.265361 | 0.572904 | 0.254674 | -0.136334 | -0.013913 | 0.110379 | 0.206236 | -0.162949 | -0.031152 | -0.026945 | 0.265177 | 0.792194 | -0.139442 | -0.181791 | -0.320064 | -0.864042 | -0.889622 | -0.231562 | -0.231211 | -0.053209 | -0.314387 | -0.526181 | -0.214719 | -0.953702 | 0.811879 | 2.322390 | 1.179342 | 1.283542 | 0.857162 | 1.283469 | 2.065088 | -0.176527 | -0.246518 | -0.309123 | -0.480496 | -0.153830 | -1.508181 | -0.504938 | -0.061989 | -1.032754 | 1.833175 | -0.164773 | -0.315023 | -0.047484 | -0.448869 | 0.007879 | -1.104980 | -0.804119 | -0.445896 | -0.860670 | -0.056288 | -0.007390 | -0.02527 | -0.707603 | 2.177721 | -0.02527 | -0.769603 | -0.318457 | 3.630806 | 1.853734 | -0.648933 | 0.582992 | -0.395199 | 0.070038 | 0.159379 | 1.066190 | 0.660254 | -0.132713 | 0.101235 | -0.498297 | 2.227862 | 0.109652 | 0.536435 | 0.948439 | -0.676833 | -0.281132 | -0.198524 | -0.067565 | 1.527628 | -0.095696 | -0.011583 | -0.650824 | -0.585286 | 1.421928 | -0.997415 | 1.398961 | -0.126007 | -0.023723 | -0.957504 | -1.072560 | -0.727890 | -0.616073 | -0.050788 | -0.305439 | 0.243818 | 0.145438 | -0.143911 | 0.055496 | 0.132373 | 0.187294 | -0.018432 | -0.031790 | -0.027232 | 0.333164 | 0.746201 | -0.085801 | -0.235946 | -0.351797 | -0.799029 | -0.881314 | -0.251566 | -0.211806 | -0.031683 | -0.270579 | -0.286378 | -0.288423 | -0.775471 | 0.773344 | 2.031070 | 0.901331 | 1.376267 | 1.328053 | 1.376132 | 1.622057 | -0.408111 | -0.329449 | -0.318948 | -0.427432 | -0.160212 | -1.226908 | -0.524183 | -0.217006 | -1.043742 | 1.556547 | -0.085843 | -0.255823 | -0.085475 | -0.466138 | -0.010925 | -0.264208 | -0.693529 | -0.339026 | -0.712527 | -0.055585 | -0.082774 | -0.02527 | -0.691957 | 2.083400 | -0.02527 | -0.889819 | -0.212280 | 3.773616 | 1.861092 | -0.692184 | 0.711622 | -0.158558 | 0.091958 | -0.132414 | 1.406877 | 0.695261 | -0.122266 | 0.375869 | -0.529929 | 2.523981 | 0.038527 | 0.456535 | 1.607301 | 0.061749 | 1.122382 | -0.580658 | -0.232199 | -0.218352 | -0.068016 | 1.738803 | -0.096371 | -0.013109 | -0.620014 | -0.620319 | 1.513442 | -1.141015 | 1.366822 | -0.549236 | -0.385926 | -1.120315 | -0.775723 | -0.174503 | -0.864877 | 0.046334 | -0.268316 | 0.510199 | 0.214902 | -1.114137 | 0.184403 | 0.097737 | 0.285913 | -0.483883 | -0.025506 | -0.029000 | 0.253461 | 0.701988 | -0.132235 | -0.157359 | -0.221336 | -0.552380 | -0.720404 | -0.025106 | -0.178849 | -0.106450 | -0.168842 | -0.497707 | -0.180904 | -0.862235 | 0.793380 | 2.359190 | 0.927465 | 1.243218 | 0.829045 | 1.324064 | 2.234098 | -0.201306 | -0.241890 | -0.260793 | -0.483300 | -0.088055 | -1.471449 | -0.665182 | -0.114351 | -1.044662 | 0.844309 | -0.174256 | 1.040027 | -0.030644 | -0.462016 | 0.058203 | -1.352284 | -0.889067 | -0.984289 | -0.806010 | 0.720941 | -0.044264 | -0.02527 | -0.755955 | 2.452948 | -1.140828 | 0.214695 | -1.011760 | 0.733318 | -0.599946 | 2.224655 | -0.632402 | -1.199061 | 0.377793 | 1.017787 | 0.691072 | -0.122839 | -0.048827 | -0.698259 | 2.431221 | -0.809806 | -0.745562 | 2.275389 | -0.836511 | -0.266711 | -0.187140 | -0.112621 | 1.642898 | -0.093240 | 0.046460 | -0.622954 | -0.584746 | 1.292987 | -0.995221 | 1.421923 | -0.010821 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | 0.932762 | 1.150527 | -0.245923 | 0.142104 | -0.198529 | 0.090654 | 0.075961 | -0.268656 | -0.001188 | -0.124810 | -0.237757 | 0.102057 | -0.376990 | -0.751178 | -0.677744 | -0.745482 | 0.183094 | -0.212815 | -0.434630 | -0.289437 | -0.398435 | -0.314028 | -0.405707 | -0.362419 | 0.256816 | 1.205944 | -0.261137 | -0.323417 | -0.265730 | -0.406218 | -0.263493 | -0.460054 | 0.530183 | 0.406734 | 0.444748 | 0.385113 | -0.960123 | 0.411970 | 0.250272 | 1.156846 | -1.0 |
| 2 | -1.114000 | 0.798901 | -0.481447 | 0.688278 | -0.047447 | -0.906768 | 0.254699 | -0.260885 | 0.327222 | 0.124169 | -0.229467 | 0.633530 | 0.182742 | 0.210657 | -0.247041 | -0.335860 | 0.956559 | 0.272774 | 0.433058 | 0.272299 | 0.008186 | -0.174554 | -0.558009 | 0.610045 | 0.480463 | 0.755573 | -0.684173 | -0.080607 | -0.326993 | -0.138873 | -0.286600 | -0.224141 | -0.363423 | -0.172607 | 0.363415 | -1.258050 | -4.712218 | -0.036862 | -2.246208 | 0.890441 | 1.418810 | -0.873898 | -0.629779 | 0.092064 | 0.639469 | 0.245998 | 0.580178 | 0.465101 | -1.949961 | -1.521941 | 3.105340 | -2.200093 | -1.082980 | -0.070136 | 2.195150 | 1.559854 | -0.928650 | -0.101503 | -0.378029 | -0.879033 | -0.400497 | 0.331273 | -0.051450 | -0.387222 | 0.720979 | -0.619174 | -0.02527 | -0.040979 | -0.097671 | 0.060580 | 0.735434 | -0.384957 | -1.945266 | 0.173491 | 0.632133 | -0.674475 | 0.614856 | 1.375363 | 0.434430 | -2.278834 | -0.758269 | 1.256667 | 0.604312 | 0.028884 | -0.252720 | 0.167111 | 1.345008 | -0.690935 | -0.292444 | 1.921173 | 1.751085 | -0.421252 | 0.195611 | 2.348246 | -1.865393 | -0.032315 | 0.255517 | -0.324715 | -0.716348 | 0.233348 | -1.840563 | -0.073477 | -0.588677 | 0.291787 | -0.022382 | 0.383145 | 0.717988 | 1.141410 | 1.043418 | -1.127641 | 1.021946 | 1.365563 | -1.090267 | 1.358969 | -1.265468 | 0.303047 | 0.298001 | 0.903619 | -0.003570 | -1.118492 | -0.858907 | 0.894818 | -0.503727 | -0.219019 | -0.502118 | -1.016321 | -0.419776 | -0.050587 | 0.478545 | -0.287900 | -0.727611 | -0.577581 | 0.315995 | 0.173117 | -0.010928 | -0.615708 | -0.180813 | -0.030189 | 0.618730 | 0.097044 | -0.032163 | -0.105984 | -0.326173 | 0.405388 | -0.780544 | -0.411307 | 0.384177 | -0.044955 | -0.451997 | -0.615570 | 0.259843 | -0.029204 | -0.412022 | 2.529548 | -1.368959 | -0.084482 | -0.009332 | -0.084481 | 1.631533 | 0.345711 | -0.242981 | -0.372576 | 0.883997 | 0.607929 | -0.548837 | 0.463937 | 0.256768 | -0.007699 | 0.778891 | -0.375135 | -0.228933 | -0.032287 | 0.730855 | -0.110343 | -0.100197 | -0.538995 | 0.061440 | -0.427558 | -0.056716 | 0.369491 | -0.02527 | -0.208705 | 0.226884 | -0.02527 | -1.313409 | -0.294272 | 0.584360 | -0.374083 | -0.768550 | 0.096134 | -1.901558 | 0.006897 | -1.149681 | -1.129717 | 1.527289 | 0.114073 | 1.081806 | -0.432682 | -0.467131 | 0.007287 | -0.078381 | 0.830538 | -0.399062 | 2.579257 | 0.043251 | -0.067565 | -0.096130 | -0.095696 | -0.007780 | 1.803386 | -0.837224 | 0.106004 | -0.740340 | 1.382304 | 0.118199 | -0.680630 | -0.262050 | -0.489862 | -1.155221 | -0.453778 | -0.050580 | 0.266885 | 0.243818 | -0.796832 | -0.437260 | 0.109644 | 0.202613 | 0.011385 | -0.612140 | -0.175256 | -0.029713 | 0.674184 | 0.139982 | -0.017296 | -0.126317 | -0.315148 | 0.270821 | -0.787130 | -0.446121 | 0.252625 | -0.014850 | -0.391616 | -0.644265 | -0.001082 | 0.086787 | -0.431800 | 2.086867 | -1.320547 | -0.004419 | 0.249766 | -0.004431 | 1.449374 | 0.700382 | -0.289362 | -0.247729 | 1.321097 | 0.547669 | -0.797464 | 0.360814 | 0.143876 | -0.312548 | 0.405156 | -0.930714 | -0.185230 | -0.079409 | 0.634651 | -0.045663 | -0.099235 | -0.459041 | -0.009066 | -0.511755 | -0.055837 | 0.282959 | -0.02527 | -0.093188 | 0.177882 | -0.02527 | -1.204169 | -0.421417 | 0.672612 | -0.428814 | -0.899036 | 0.158836 | -2.048078 | 0.173269 | -1.344811 | -0.998334 | 1.452192 | 0.082773 | 1.062767 | -0.421985 | -0.358082 | 0.076212 | -0.215666 | -0.286302 | 0.252035 | 0.935059 | -0.151565 | 1.179955 | 0.111037 | -0.068016 | -0.372888 | -0.087905 | -0.009659 | 1.205134 | -0.857237 | 0.091427 | -0.486652 | 1.686946 | -0.143640 | -0.490189 | -0.152229 | -0.547256 | -1.007130 | -0.700765 | -0.084075 | 0.532296 | -0.331785 | -0.732409 | -1.114137 | -0.950407 | 0.154926 | -0.011948 | -0.829408 | -0.203903 | -0.065512 | 0.596497 | 0.065769 | -0.043608 | -0.101012 | -0.224725 | 0.187442 | -0.638979 | -0.425033 | -0.014023 | -0.104828 | -0.191455 | -0.551876 | 0.155714 | 0.007272 | -0.431993 | 2.580271 | -1.394717 | -0.053305 | 0.014492 | -0.120376 | 1.674585 | 0.434070 | -0.243161 | -0.508667 | 0.925136 | 0.724378 | -0.552893 | 0.339124 | 0.278870 | -0.025414 | 0.237409 | -0.371878 | -0.879464 | -0.077517 | 0.798321 | -0.121402 | 0.213104 | -0.354262 | 0.320124 | -0.413930 | -0.451016 | 0.282222 | -0.02527 | -0.231820 | 0.515948 | 0.416929 | -0.281560 | -1.011760 | -0.043159 | 1.561412 | -0.623339 | -0.643117 | -0.093625 | -0.953304 | -1.158939 | 1.431491 | 0.087593 | 1.232679 | -0.438207 | -0.570085 | -0.809806 | -0.745562 | 4.913421 | -0.095153 | 2.318787 | 0.046768 | -0.112621 | -0.028099 | -0.091359 | 0.067736 | 1.704469 | -0.833253 | 0.032352 | -0.704821 | 1.403839 | -0.106040 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | -0.625302 | 1.655967 | -0.241481 | -0.724161 | -0.163297 | -0.295519 | -0.700794 | -0.247552 | -0.227419 | -1.060978 | -0.223482 | -0.274599 | 6.050806 | 0.229121 | 0.986789 | -0.396072 | 0.584471 | 2.056521 | -2.097595 | 6.467174 | -2.220023 | 6.587355 | -2.128830 | 5.149517 | 0.257279 | -0.263745 | -0.199823 | -0.633805 | -0.188395 | -0.600996 | -0.205046 | -0.590505 | -1.262799 | 0.022320 | 0.014418 | 0.029888 | 2.991195 | 3.627143 | 3.321511 | -0.178955 | 1.0 |
| 3 | -0.350156 | -0.199072 | -0.051705 | -1.104376 | -0.050831 | 0.502662 | -0.013974 | 0.343240 | -0.765369 | -0.370817 | -0.116929 | 0.581382 | 0.214786 | 0.535203 | -0.089594 | -0.227950 | 0.759334 | -0.013593 | -0.236334 | 0.239564 | -0.173036 | -0.513969 | -0.468610 | 0.618498 | 0.344715 | 0.542987 | -1.898787 | 0.681327 | 0.696934 | -0.545133 | -0.210758 | -0.209330 | -0.061360 | -0.172179 | 0.061352 | -3.376184 | -0.335669 | -0.905372 | 0.369643 | -0.883858 | 1.202003 | -0.575486 | -0.629779 | 0.016866 | 0.657936 | 0.568511 | 0.592588 | 0.533265 | -1.949961 | -1.521941 | 3.105340 | -2.200093 | -1.082980 | -0.070136 | 2.253475 | 1.048099 | -0.772960 | -0.037099 | -0.060112 | -0.990738 | -0.514423 | 0.322201 | -0.051103 | 0.051130 | 0.650680 | -1.079548 | -0.02527 | -2.198007 | -0.507540 | -1.057708 | 1.129208 | 2.803165 | -1.775346 | 1.417134 | 0.604189 | 0.272180 | -0.577913 | -0.039430 | 0.621291 | -1.041069 | 0.136926 | -1.595576 | 0.768908 | -0.249932 | -0.518768 | -0.408413 | 1.345008 | 0.109654 | 0.083944 | -0.336009 | -0.504062 | 0.032738 | -1.130254 | 1.270390 | 0.370180 | 0.249355 | 0.523856 | -0.533241 | -1.238507 | 0.131031 | 0.904923 | -0.073477 | -0.782633 | 0.365522 | 0.005798 | 0.395588 | 0.595024 | 1.469741 | -2.481235 | -0.850243 | -2.034494 | -1.798317 | 0.648179 | -1.071141 | -0.969862 | -0.277972 | 0.686669 | -0.251058 | -0.761352 | 1.163249 | -3.619035 | 0.574438 | 0.157872 | -0.375183 | 0.019750 | -0.796588 | -0.967602 | -0.050854 | -0.319103 | -0.913940 | -0.097774 | -0.917001 | 0.288502 | 0.058713 | 0.256080 | 0.698097 | 0.038738 | 0.004610 | 0.329459 | 0.817230 | -0.139442 | 0.149230 | -0.023774 | -0.348608 | -0.149214 | -0.273253 | -0.592239 | -0.082098 | -0.373363 | -0.973127 | -0.531093 | -0.806623 | -0.806502 | 1.615764 | -1.390359 | -0.819057 | -1.432934 | -0.819016 | 0.676679 | 0.006457 | -0.016619 | -0.843942 | 0.652122 | 0.934740 | -0.600068 | 0.306847 | -0.233199 | 0.030654 | 0.431442 | -0.375135 | -0.176004 | 0.333381 | 1.317921 | -0.054279 | -0.465887 | -0.375992 | 0.083585 | -0.330214 | -0.056310 | 0.044713 | -0.02527 | 0.091436 | 1.131457 | -0.02527 | 0.864211 | -0.419228 | 2.265488 | -0.472928 | -0.220452 | -0.188695 | -0.751248 | 0.472834 | 1.511869 | -0.580741 | 0.673130 | -0.130925 | 0.530337 | -0.020448 | -0.512190 | -0.364951 | -0.274005 | 1.125291 | -0.399062 | -0.436012 | -0.035986 | -0.067565 | -0.285006 | -0.087687 | -0.022278 | 0.333557 | -0.738639 | 0.144244 | -0.652394 | 1.683911 | -0.506155 | 0.552980 | -0.460407 | 0.175113 | -0.850145 | -1.005496 | -0.050899 | -0.026961 | -1.231327 | 0.031971 | -1.008518 | 0.380386 | 0.062134 | 0.240941 | 0.807560 | -0.021094 | 0.002861 | 0.333164 | 0.843233 | -0.116906 | 0.179078 | 0.029053 | -0.338329 | -0.154900 | -0.298812 | -0.549999 | -0.120434 | -0.328129 | -0.842957 | -0.500255 | -0.809961 | -0.766648 | 1.453921 | -1.411156 | -1.039023 | -1.439152 | -1.038942 | 0.961256 | -0.001548 | 0.063745 | -0.523469 | 0.383941 | 0.824090 | -0.727115 | 0.150549 | -0.250377 | -0.169813 | 0.363881 | -0.930714 | -0.152624 | 0.121099 | 1.060706 | -0.026613 | -0.043973 | -0.350735 | 0.005639 | -0.436489 | -0.055617 | 0.079832 | -0.02527 | 0.024521 | 0.655386 | -0.02527 | 1.361607 | -0.516479 | 2.107090 | -0.445230 | -0.071630 | 0.119816 | -1.130311 | 0.629518 | 1.354116 | -0.797900 | 0.751330 | -0.127267 | 0.338364 | 0.028560 | -0.166292 | -0.225270 | 0.036409 | -0.360561 | 0.109320 | 1.497027 | -0.366111 | -0.509092 | -0.095639 | -0.068016 | -0.158656 | -0.087905 | -0.015668 | 0.337441 | -0.755701 | 0.021108 | -0.813834 | 2.363797 | -0.113668 | 0.115301 | -0.356416 | 0.028161 | -0.804895 | -0.710017 | -0.234886 | -0.340277 | -0.909772 | -0.130764 | -0.008698 | -0.950407 | 0.048404 | 0.347110 | 0.516441 | 0.046677 | 0.286272 | 0.312193 | 0.715219 | -0.132603 | 0.127143 | -0.075038 | -0.246020 | -0.184645 | -0.107220 | -0.280314 | -0.115752 | -0.177845 | -0.817010 | -0.606301 | -0.759358 | -0.713858 | 1.647602 | -1.420067 | -0.806125 | -1.352105 | -0.836574 | 0.790157 | 0.011643 | 0.004157 | -0.956626 | 0.674711 | 1.061850 | -0.599599 | 0.170605 | -0.280398 | 0.012735 | -0.113536 | -0.371878 | -0.866787 | 0.275875 | 1.410823 | -0.047360 | -0.796364 | -0.067216 | 0.439115 | -0.322118 | 1.027345 | 0.021704 | -0.02527 | 0.087546 | 2.072138 | -0.333672 | -0.549396 | 0.432990 | -0.511979 | -0.691022 | -0.632172 | -1.410071 | 0.288995 | 1.198146 | -0.552939 | 0.669298 | -0.122128 | 0.574589 | -0.014720 | -0.367764 | -0.809806 | 1.458969 | 1.535095 | -0.836511 | -0.409237 | -0.044627 | -0.112621 | -0.205425 | -0.085908 | -0.010296 | 0.308905 | -0.733787 | 0.048082 | -0.702659 | 1.787174 | -0.635529 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | 0.409315 | -6.626947 | -0.943318 | 2.018285 | 25.273849 | 30.662679 | 1.644833 | 24.582485 | 31.051487 | 3.239803 | 26.607528 | 30.846836 | 1.984363 | 2.002900 | 3.852192 | 1.602703 | 0.751806 | -0.211916 | 0.450099 | 0.294966 | 1.079847 | 0.296140 | 0.385749 | 0.492947 | 0.002548 | -0.278290 | -0.221613 | -0.691776 | -0.232808 | -0.770689 | -0.224950 | -0.645708 | -0.322218 | -0.292200 | -0.362121 | -0.283360 | -0.101845 | -0.178804 | -0.308135 | -0.275049 | -1.0 |
| 4 | 0.242296 | 0.087328 | 1.117227 | -0.156616 | -0.047033 | -0.115954 | 0.187531 | 0.545066 | -0.149545 | -0.790478 | -0.599235 | 0.610329 | 0.558822 | 0.436541 | 0.179609 | 0.170486 | 0.560597 | -0.035232 | -0.971470 | 0.226789 | -0.217071 | -0.131402 | 0.143217 | 0.477615 | 0.278161 | 0.511055 | -1.927707 | 1.933338 | 2.595907 | -0.747983 | 0.488609 | -0.143239 | -0.283042 | -0.157178 | 0.283034 | -0.220697 | -1.544931 | 1.526523 | 0.348307 | -0.486722 | -0.352717 | 2.160530 | 5.044562 | 4.610846 | -0.759292 | -0.416700 | 4.108142 | 1.249991 | 0.565437 | 0.932419 | 0.343601 | 0.139051 | -1.035010 | 0.566594 | -1.603330 | -0.012134 | -2.291208 | 3.196132 | 1.125286 | 2.367580 | 2.396185 | 3.357289 | -0.051267 | -0.183091 | 2.801278 | -1.218466 | -0.02527 | -2.103044 | 1.447684 | -0.308969 | -0.658571 | -0.000635 | 0.011878 | 0.522895 | -0.303990 | 0.096184 | 0.217266 | 1.370004 | 0.605720 | 0.384333 | -0.113573 | 0.479856 | 0.484295 | -2.542422 | 0.146352 | -0.408413 | -1.532954 | 0.921198 | -1.530722 | -0.291280 | 1.187298 | -1.329232 | 1.090133 | 1.727662 | 0.252518 | 2.596610 | -0.180534 | -0.895297 | -2.229684 | 0.079873 | -0.249335 | -0.073477 | 1.005673 | 0.386589 | -0.037036 | -0.885992 | -0.891723 | 0.355990 | -0.467148 | -0.832490 | -0.923061 | -0.743690 | -0.853563 | 1.093794 | -0.857250 | 0.405580 | -0.013016 | 0.400423 | 1.422844 | 0.999190 | -1.674474 | -0.417387 | 0.018588 | 0.093308 | 0.008238 | -0.715202 | -0.456913 | -0.050491 | -0.848040 | -0.053135 | -0.923416 | -0.698264 | 0.334323 | 0.243235 | 0.307904 | 0.677096 | -0.049593 | -0.032225 | 0.072330 | 1.186248 | -0.067923 | -0.054183 | -0.186682 | -0.473984 | -0.547278 | -0.511640 | 0.055970 | -0.362736 | -0.341418 | 0.993435 | -0.689280 | -0.491453 | 0.498318 | -0.688398 | -0.937657 | -0.208081 | 0.071150 | -0.208074 | 0.434094 | -1.521526 | 1.865017 | -1.140057 | -1.180578 | 0.016784 | 1.617958 | -0.235251 | -0.471223 | 0.883704 | 1.333731 | -0.093808 | -0.201512 | -0.411253 | -0.070538 | -0.049404 | 0.250497 | -0.454548 | -0.117704 | 0.215739 | -0.056222 | 0.003899 | -0.02527 | -0.034577 | 0.679992 | -0.02527 | 0.066469 | -0.245902 | 0.817229 | 0.905201 | -0.061557 | 0.781710 | 0.348198 | 0.653548 | 0.309845 | -0.153759 | 0.836236 | -0.150596 | -0.973833 | 1.972255 | 0.863000 | 0.332995 | 0.871789 | 1.125291 | -1.024046 | -0.281132 | -0.023796 | -0.067565 | 0.724924 | -0.098365 | -0.032139 | -0.695773 | 0.816805 | 0.010407 | -0.736958 | 0.049852 | -0.140280 | 0.580287 | 0.555771 | 0.666680 | -0.705437 | -0.554910 | -0.050595 | -0.937810 | 0.538847 | -0.757365 | -0.993079 | 0.217941 | 0.261146 | 0.276529 | 1.162745 | -0.062011 | -0.032235 | 0.026246 | 1.095318 | 0.016031 | -0.071503 | -0.173164 | -0.440915 | -0.546668 | -0.531148 | 0.182493 | -0.322420 | -0.323104 | 1.326377 | -0.676612 | -0.575427 | 0.339739 | -0.861651 | -0.694167 | 0.236017 | 0.029343 | 0.235984 | 0.056396 | -1.360234 | 1.683432 | -1.062180 | -1.327268 | -0.226787 | 1.442789 | -0.326471 | -0.539177 | 0.769148 | 1.479864 | -0.016358 | -0.172177 | -0.437904 | 0.052665 | -0.046530 | 0.005874 | -0.415683 | -0.078703 | 0.198921 | -0.055474 | 0.099232 | -0.02527 | 0.033384 | 0.995632 | -0.02527 | -0.006240 | -0.297836 | 0.686675 | 1.011611 | 0.173528 | 0.919729 | 0.435291 | 0.534655 | 0.657919 | 0.003837 | 1.227916 | -0.142270 | -1.108754 | 1.835437 | 1.187769 | 0.415380 | 1.212762 | 0.530546 | -0.223680 | 1.684350 | -0.795204 | -0.232199 | 0.039992 | -0.068016 | 1.284098 | -0.096371 | -0.028660 | -0.664895 | 1.071951 | 0.041943 | -0.720353 | 0.149829 | -0.169488 | 0.016595 | 0.080387 | -0.004753 | -0.784307 | -0.465444 | -0.024438 | -0.761740 | -0.079994 | -0.980586 | -1.114137 | 1.713158 | 0.223769 | 0.416226 | 0.232819 | -0.053951 | -0.107767 | 0.060975 | 1.052813 | -0.071977 | -0.046079 | -0.156775 | -0.319343 | -0.440232 | -0.910799 | -0.097479 | -0.214219 | -0.172451 | 1.360716 | -0.861070 | -0.865110 | 0.603490 | -0.732689 | -0.949346 | -0.183654 | 0.088483 | -0.237263 | 0.439680 | -1.505619 | 1.808222 | -1.125250 | -1.223283 | -0.565664 | 1.214481 | -0.102277 | -0.525081 | 0.733556 | 0.127330 | -0.099798 | -0.777246 | -0.388966 | -0.012880 | -0.216080 | -1.007588 | -0.995922 | -0.746529 | 0.105504 | 1.513749 | -0.005524 | -0.02527 | -0.084140 | 1.812996 | -0.525826 | 0.371939 | 1.170660 | 0.186073 | -1.058096 | 1.334413 | 1.582335 | -1.199061 | 0.208094 | -0.168459 | 0.761221 | -0.139003 | -0.928427 | 3.219844 | 0.781380 | 0.093284 | -0.745562 | 2.451447 | -0.836511 | -0.266711 | -0.025401 | -0.112621 | 0.563636 | -0.094615 | -0.061635 | -0.657819 | 0.808501 | -0.011237 | -0.781583 | 0.079291 | -0.045134 | -0.587398 | -0.437266 | 0.307134 | -0.376422 | 1.242477 | -0.770061 | 1.186942 | 0.658373 | 0.600786 | 0.613597 | 0.650256 | 1.049070 | 0.606361 | 0.428173 | 0.612497 | 0.523328 | -0.205168 | -0.546970 | -0.510902 | -0.538198 | 0.183094 | -0.212815 | -0.434630 | -0.289437 | -0.398435 | -0.314028 | -0.405707 | -0.362419 | 0.085279 | -0.270290 | -0.227409 | -0.496123 | -0.222385 | -0.503607 | -0.230791 | -0.454486 | -5.906917 | 26.867221 | 27.071429 | 26.913337 | -0.101845 | -0.178804 | -0.308135 | -0.275049 | -1.0 |
nxp = nsdp.drop('Pass/Fail',axis=1)
nyp=nsdp['Pass/Fail']
print('''\n\033[1m''' + '''Spliting New dataset in Train and Test sets''' + '''\033[0m''')
nxp_train,nxp_test,nyp_train, nyp_test = train_test_split(nx, ny,train_size=0.7, test_size=0.3,random_state=202)
print('New X_Train',len(nxp_train),'\nNew X_Test',len(nxp_test))
print('New Y_Train',len(nyp_train),'\nNew Y_Test',len(nyp_test))
Spliting New dataset in Train and Test sets
New X_Train 2048
New X_Test 878
New Y_Train 2048
New Y_Test 878
print('''\n\033[1m''' + '''\t\tLOGISTIC REGRESSION''' + '''\033[0m''')
print('''\n\033[1m''' + '''WITH PCA AND IMBALANCE TARGET SET''' + '''\033[0m''')
model = LogisticRegression()
model.fit(nxp_train, nyp_train)
model.fit(nxp_test, nyp_test)
nyp_pred = model.predict(nxp_test)
model_train = model.score(nxp_train,nyp_train)
model_test = model.score(nxp_test,nyp_test)
score_train['LOG-REG PCA TRAIN'] = model_train
score_test['LOG-REG PCA TEST'] = model_test
print('''\n\033[1m''' + '''LOG-REG PCA TRAIN SCORE''' + '''\033[0m''',model_train)
print('''\n\033[1m''' + '''LOG-REG PCA TEST SCORE''' + '''\033[0m''',model_test)
print('''\033[1m''' + '''\t\tLOG-REG PCA MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',metrics.classification_report(nyp_test, nyp_pred))
print('''\n\n\033[1m''' + '''LOG-REG PCA MODEL CONFUSION METRICS WITH HEATMAP\n''' + '''\033[0m''',metrics.confusion_matrix(nyp_test, nyp_pred))
cm3 = metrics.confusion_matrix(nyp_test, nyp_pred)
plt.figure(figsize = (5,3))
sns.heatmap(cm3, annot=True,cmap='Blues', fmt='g')
LOGISTIC REGRESSION WITH PCA AND IMBALANCE TARGET SET LOG-REG PCA TRAIN SCORE 0.8193359375 LOG-REG PCA TEST SCORE 0.907744874715262 LOG-REG PCA MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 0.93 0.87 0.90 415 1.0 0.89 0.94 0.92 463 accuracy 0.91 878 macro avg 0.91 0.91 0.91 878 weighted avg 0.91 0.91 0.91 878 LOG-REG PCA MODEL CONFUSION METRICS WITH HEATMAP [[361 54] [ 27 436]]
<AxesSubplot:>
print('''\033[1m''' + '''\t\tLOG-REG PCA MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',metrics.classification_report(nyp_test, nyp_pred))
print('''\n\033[1m''' + '''LOG-REG PCA TRAIN SCORE''' + '''\033[0m''',model.score(nxp_train,nyp_train))
print('''\n\033[1m''' + '''LOG-REG PCA TEST SCORE''' + '''\033[0m''',model.score(nxp_test,nyp_test))
print('=====================================================================')
print('''\033[1m''' + '''\n\t\tLOG-REG TUINING MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',metrics.classification_report(ny_test, ny_predict))
print('''\n\033[1m''' + '''LOG-REG TUINING TRAIN SCORE''' + '''\033[0m''',model.score(nx_train,ny_train))
print('''\n\033[1m''' + '''LOG-REG TUINING TEST SCORE''' + '''\033[0m''',model.score(nx_test,ny_test))
LOG-REG PCA MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 0.93 0.87 0.90 415 1.0 0.89 0.94 0.92 463 accuracy 0.91 878 macro avg 0.91 0.91 0.91 878 weighted avg 0.91 0.91 0.91 878 LOG-REG PCA TRAIN SCORE 0.8193359375 LOG-REG PCA TEST SCORE 0.907744874715262 ===================================================================== LOG-REG TUINING MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 0.93 0.87 0.90 415 1.0 0.89 0.94 0.92 463 accuracy 0.91 878 macro avg 0.91 0.91 0.91 878 weighted avg 0.91 0.91 0.91 878 LOG-REG TUINING TRAIN SCORE 0.8193359375 LOG-REG TUINING TEST SCORE 0.907744874715262
acc_arr = [] #Array for Accuracy values
mis_arr = [] #Array for Misclassification values
sen_arr = [] #Array for Sensitivity values
spec_arr = [] #Array for Specificity values
prec_arr = [] #Array for Precision values
#User defined function to find the confusion matrix values
def ConfusionMatrix(matrix):
for i in range(0,len(matrix)):
for j in range(0,len(matrix)):
if i == 0:
if j == 0:
tn = matrix[i][j]
else:
fp = matrix[i][j]
if i == 1:
if j == 0:
fn = matrix[i][j]
else:
tp = matrix[i][j]
j = 0
return tp,tn,fp,fn
TP,TN,FP,FN = ConfusionMatrix(cm1)
Accuracy=(TP+TN)/(TP+TN+FP+FN)
print('Accuracy of logistic regression classifier on test set: {:.2%}'.format(Accuracy))
acc_arr.append(Accuracy)
Misclassification_Rate=(FP+FN)/(TP+TN+FP+FN)
print('Misclassification Rate: It is often wrong: {:.2%}'.format(Misclassification_Rate))
mis_arr.append(Misclassification_Rate)
#Recall
Sensitivity=TP/(FN+TP)
print('Sensitivity: When its actually yes how often it predicts yes: {:.2%}'.format(Sensitivity))
sen_arr.append(Sensitivity)
Specificity=TN/(TN+FP)
print('Specificity: When its actually no, how often does it predict no: {:.2%}'.format(Specificity))
spec_arr.append(Specificity)
Precision=TP/(FP+TP)
print('Precision: When it predicts yes, how often is it correct: {:.2%}'.format(Precision))
prec_arr.append(Precision)
Accuracy of logistic regression classifier on test set: 90.77% Misclassification Rate: It is often wrong: 9.23% Sensitivity: When its actually yes how often it predicts yes: 94.17% Specificity: When its actually no, how often does it predict no: 86.99% Precision: When it predicts yes, how often is it correct: 88.98%
TP,TN,FP,FN = ConfusionMatrix(cm2)
Accuracy=(TP+TN)/(TP+TN+FP+FN)
print('Accuracy of logistic regression classifier test set after tuning: {:.2%}'.format(Accuracy))
acc_arr.append(Accuracy)
Misclassification_Rate=(FP+FN)/(TP+TN+FP+FN)
print('Misclassification Rate: It is often wrong: {:.2%}'.format(Misclassification_Rate))
mis_arr.append(Misclassification_Rate)
#Recall
Sensitivity=TP/(FN+TP)
print('Sensitivity: When its actually yes how often it predicts yes: {:.2%}'.format(Sensitivity))
sen_arr.append(Sensitivity)
Specificity=TN/(TN+FP)
print('Specificity: When its actually no, how often does it predict no: {:.2%}'.format(Specificity))
spec_arr.append(Specificity)
Precision=TP/(FP+TP)
print('Precision: When it predicts yes, how often is it correct: {:.2%}'.format(Precision))
prec_arr.append(Precision)
Accuracy of logistic regression classifier test set after tuning: 90.89% Misclassification Rate: It is often wrong: 9.11% Sensitivity: When its actually yes how often it predicts yes: 94.17% Specificity: When its actually no, how often does it predict no: 87.23% Precision: When it predicts yes, how often is it correct: 89.16%
TP,TN,FP,FN = ConfusionMatrix(cm3)
Accuracy=(TP+TN)/(TP+TN+FP+FN)
print('Accuracy of logistic regression classifier test set after pca: {:.2%}'.format(Accuracy))
acc_arr.append(Accuracy)
Misclassification_Rate=(FP+FN)/(TP+TN+FP+FN)
print('Misclassification Rate: It is often wrong: {:.2%}'.format(Misclassification_Rate))
mis_arr.append(Misclassification_Rate)
#Recall
Sensitivity=TP/(FN+TP)
print('Sensitivity: When its actually yes how often it predicts yes: {:.2%}'.format(Sensitivity))
sen_arr.append(Sensitivity)
Specificity=TN/(TN+FP)
print('Specificity: When its actually no, how often does it predict no: {:.2%}'.format(Specificity))
spec_arr.append(Specificity)
Precision=TP/(FP+TP)
print('Precision: When it predicts yes, how often is it correct: {:.2%}'.format(Precision))
prec_arr.append(Precision)
Accuracy of logistic regression classifier test set after pca: 90.77% Misclassification Rate: It is often wrong: 9.23% Sensitivity: When its actually yes how often it predicts yes: 94.17% Specificity: When its actually no, how often does it predict no: 86.99% Precision: When it predicts yes, how often is it correct: 88.98%
print('''\n\033[1m''' + '''\t\t k-Nearest Neighbours''' + '''\033[0m''')
print('Creating odd list of K for KNN')
myList = list(range(1,50))
print('Subsetting just the odd ones')
neighbors = list(filter(lambda x: x % 2 != 0, myList))
k-Nearest Neighbours
Creating odd list of K for KNN
Subsetting just the odd ones
ac_scores = []
for k in neighbors:
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(nx_train, ny_train)
y_Pred = knn.predict(nx_test)
scores = accuracy_score(ny_test, y_Pred)
ac_scores.append(scores)
MSE = [1 - x for x in ac_scores]
optimal_k = neighbors[MSE.index(min(MSE))]
print("The optimal number of neighbors is %d" % optimal_k)
The optimal number of neighbors is 1
knn = KNeighborsClassifier(n_neighbors= optimal_k , weights = 'distance' )
knn.fit(nx_train, ny_train)
y_pred = knn.predict(nx_test)
model_train = knn.score(nx_train,ny_train)
model_test = knn.score(nx_test,ny_test)
score_train['kNN TRAIN'] = model_train
score_test['kNN TEST'] = model_test
print('''\n\033[1m''' + '''kNN TRAIN SCORE''' + '''\033[0m''',model_train)
print('''\n\033[1m''' + '''kNN TEST SCORE''' + '''\033[0m''',model_test)
print('''\033[1m''' + '''\t\tkNN MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',classification_report(ny_test, y_pred))
print('''\n\n\033[1m''' + '''kNN MODEL CONFUSION METRICS WITH HEATMAP\n''' + '''\033[0m''',confusion_matrix(ny_test, y_pred))
cm4 = metrics.confusion_matrix(ny_test, y_pred)
plt.figure(figsize = (5,3))
sns.heatmap(cm4, annot=True,cmap='Blues', fmt='g')
kNN TRAIN SCORE 1.0 kNN TEST SCORE 0.9738041002277904 kNN MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 1.00 0.94 0.97 415 1.0 0.95 1.00 0.98 463 accuracy 0.97 878 macro avg 0.98 0.97 0.97 878 weighted avg 0.98 0.97 0.97 878 kNN MODEL CONFUSION METRICS WITH HEATMAP [[392 23] [ 0 463]]
<AxesSubplot:>
model = KNeighborsClassifier()
n_neighbors = range(1, 21, 2)
weights = ['uniform', 'distance']
metric = ['euclidean', 'manhattan', 'minkowski']
# define grid search
grid = dict(n_neighbors=n_neighbors,weights=weights,metric=metric)
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
randomCV = RandomizedSearchCV(model, param_distributions=grid, n_iter=10,cv=cv,
scoring='accuracy',error_score=0, iid=True)
#grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv,
# scoring='accuracy',error_score=0, iid=True)
grid_result =randomCV.fit(nx, ny)
# summarize results
print(f"Best model score of Random Forest using {grid_result.best_params_} is {grid_result.best_score_:.3f}")
Best model score of Random Forest using {'weights': 'distance', 'n_neighbors': 3, 'metric': 'minkowski'} is 0.931
pipe= make_pipeline(StandardScaler(),(KNeighborsClassifier(weights='uniform', n_neighbors=2)))
pipe.fit(X_train_d, y_train_d)
pipe.fit(X_test_d, y_test_d)
Pipeline(steps=[('standardscaler', StandardScaler()),
('kneighborsclassifier', KNeighborsClassifier(n_neighbors=2))])
print('''\n\033[1m''' + '''Checking the achieved test accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved test accuracy of kNN"
Ha = "There is a change in the achieved test accuracy of kNN"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved test accuracies with the different sample population
No change in the achieved test accuracy of kNN as the p_value (nan) > 0.05
print('''\n\033[1m''' + '''Checking the achieved train accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved train accuracy of kNN"
Ha = "There is a change in the achieved train accuracy of kNN"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved train accuracies with the different sample population
No change in the achieved train accuracy of kNN as the p_value (nan) > 0.05
TP,TN,FP,FN = ConfusionMatrix(cm4)
Accuracy=(TP+TN)/(TP+TN+FP+FN)
print('Accuracy of kNN classifier test set: {:.2%}'.format(Accuracy))
acc_arr.append(Accuracy)
Misclassification_Rate=(FP+FN)/(TP+TN+FP+FN)
print('Misclassification Rate: It is often wrong: {:.2%}'.format(Misclassification_Rate))
mis_arr.append(Misclassification_Rate)
#Recall
Sensitivity=TP/(FN+TP)
print('Sensitivity: When its actually yes how often it predicts yes: {:.2%}'.format(Sensitivity))
sen_arr.append(Sensitivity)
Specificity=TN/(TN+FP)
print('Specificity: When its actually no, how often does it predict no: {:.2%}'.format(Specificity))
spec_arr.append(Specificity)
Precision=TP/(FP+TP)
print('Precision: When it predicts yes, how often is it correct: {:.2%}'.format(Precision))
prec_arr.append(Precision)
Accuracy of kNN classifier test set: 97.38% Misclassification Rate: It is often wrong: 2.62% Sensitivity: When its actually yes how often it predicts yes: 100.00% Specificity: When its actually no, how often does it predict no: 94.46% Precision: When it predicts yes, how often is it correct: 95.27%
svm =SVC(gamma=0.025, C=3)
svm.fit(nx_train, ny_train)
svm.score(nx_test, ny_test)
y_pred = svm.predict(nx_test)
model_train = svm.score(nx_train,ny_train)
model_test = svm.score(nx_test,ny_test)
score_train['SVM TRAIN'] = model_train
score_test['SVM TEST'] = model_test
print('''\n\033[1m''' + '''SVM TRAIN SCORE''' + '''\033[0m''',model_train)
print('''\n\033[1m''' + '''SVM TEST SCORE''' + '''\033[0m''',model_test)
print('''\033[1m''' + '''\t\tSVM MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',classification_report(ny_test, y_pred))
print('''\n\n\033[1m''' + '''SVM MODEL CONFUSION METRICS WITH HEATMAP\n''' + '''\033[0m''',confusion_matrix(ny_test, y_pred))
cm5 = metrics.confusion_matrix(ny_test, y_pred)
plt.figure(figsize = (5,3))
sns.heatmap(cm5, annot=True,cmap='Blues', fmt='g')
SVM TRAIN SCORE 1.0 SVM TEST SCORE 1.0 SVM MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 1.00 1.00 1.00 415 1.0 1.00 1.00 1.00 463 accuracy 1.00 878 macro avg 1.00 1.00 1.00 878 weighted avg 1.00 1.00 1.00 878 SVM MODEL CONFUSION METRICS WITH HEATMAP [[415 0] [ 0 463]]
<AxesSubplot:>
model = SVC()
kernel = ['poly', 'rbf', 'sigmoid']
C = [50, 10, 1.0, 0.1, 0.01]
gamma = [0.001, 0.01, 0.1, 1, 10]
# define grid search
grid = dict(kernel=kernel, C=C, gamma=gamma)
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
randomCV = RandomizedSearchCV(model, param_distributions=grid, n_iter=10,cv=cv,
scoring='accuracy',error_score=0, iid=True)
grid_result =randomCV.fit(nx, ny)
# summarize results
print(f"Best model score of Random Forest using {grid_result.best_params_} is {grid_result.best_score_:.3f}")
Best model score of Random Forest using {'kernel': 'rbf', 'gamma': 10, 'C': 50} is 1.000
pipe= make_pipeline(StandardScaler(),(SVC(gamma=0.025, C=3) ))
pipe.fit(X_train_d, y_train_d)
pipe.fit(X_test_d, y_test_d)
Pipeline(steps=[('standardscaler', StandardScaler()),
('svc', SVC(C=3, gamma=0.025))])
print('''\n\033[1m''' + '''Checking the achieved train accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved test accuracy of SVM"
Ha = "There is a change in the achieved test accuracy of SVM"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved train accuracies with the different sample population
No change in the achieved test accuracy of SVM as the p_value (nan) > 0.05
print('''\n\033[1m''' + '''Checking the achieved train accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved train accuracy of SVM"
Ha = "There is a change in the achieved train accuracy of SVM"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved train accuracies with the different sample population
No change in the achieved train accuracy of SVM as the p_value (nan) > 0.05
TP,TN,FP,FN = ConfusionMatrix(cm5)
Accuracy=(TP+TN)/(TP+TN+FP+FN)
print('Accuracy of SVM classifier test set: {:.2%}'.format(Accuracy))
acc_arr.append(Accuracy)
Misclassification_Rate=(FP+FN)/(TP+TN+FP+FN)
print('Misclassification Rate: It is often wrong: {:.2%}'.format(Misclassification_Rate))
mis_arr.append(Misclassification_Rate)
#Recall
Sensitivity=TP/(FN+TP)
print('Sensitivity: When its actually yes how often it predicts yes: {:.2%}'.format(Sensitivity))
sen_arr.append(Sensitivity)
Specificity=TN/(TN+FP)
print('Specificity: When its actually no, how often does it predict no: {:.2%}'.format(Specificity))
spec_arr.append(Specificity)
Precision=TP/(FP+TP)
print('Precision: When it predicts yes, how often is it correct: {:.2%}'.format(Precision))
prec_arr.append(Precision)
Accuracy of SVM classifier test set: 100.00% Misclassification Rate: It is often wrong: 0.00% Sensitivity: When its actually yes how often it predicts yes: 100.00% Specificity: When its actually no, how often does it predict no: 100.00% Precision: When it predicts yes, how often is it correct: 100.00%
dTree = DecisionTreeClassifier(criterion = 'gini', random_state=1)
dTree.fit(nx_train, ny_train)
dTree.fit(nx_test, ny_test)
y_pred = dTree.predict(nx_test)
model_train = dTree.score(nx_train,ny_train)
model_test = dTree.score(nx_test,ny_test)
score_train['DTREEE TRAIN'] = model_train
score_test['DTREE TEST'] = model_test
print('''\n\033[1m''' + '''SVM TRAIN SCORE''' + '''\033[0m''',model_train)
print('''\n\033[1m''' + '''SVM TEST SCORE''' + '''\033[0m''',model_test)
print('''\033[1m''' + '''\t\tSVM MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',classification_report(ny_test, y_pred))
print('''\n\n\033[1m''' + '''SVM MODEL CONFUSION METRICS WITH HEATMAP\n''' + '''\033[0m''',confusion_matrix(ny_test, y_pred))
cm6 = metrics.confusion_matrix(ny_test, y_pred)
plt.figure(figsize = (5,3))
sns.heatmap(cm6, annot=True,cmap='Blues', fmt='g')
SVM TRAIN SCORE 0.9091796875 SVM TEST SCORE 1.0 SVM MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 1.00 1.00 1.00 415 1.0 1.00 1.00 1.00 463 accuracy 1.00 878 macro avg 1.00 1.00 1.00 878 weighted avg 1.00 1.00 1.00 878 SVM MODEL CONFUSION METRICS WITH HEATMAP [[415 0] [ 0 463]]
<AxesSubplot:>
param_grid ={"max_depth": [5, None],
"max_features": [1,2,3,4,5,6,7,8,9],
"min_samples_leaf": [1,2,3,4,5,6,7,8,9],
"criterion": ["gini", "entropy"]}
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
randomCV = RandomizedSearchCV(dTree, param_distributions= param_grid, n_iter=10,cv=cv,
scoring='accuracy',error_score=0, iid=True)
grid_result =randomCV.fit(nx, ny)
print(f"Best model score of Random Forest using {grid_result.best_params_} is {grid_result.best_score_:.3f}")
Best model score of Random Forest using {'min_samples_leaf': 1, 'max_features': 1, 'max_depth': None, 'criterion': 'gini'} is 0.964
pipe= make_pipeline(StandardScaler(),( DecisionTreeClassifier(criterion = 'gini', random_state=1)))
pipe.fit(X_train_d, y_train_d)
pipe.fit(X_test_d, y_test_d)
Pipeline(steps=[('standardscaler', StandardScaler()),
('decisiontreeclassifier',
DecisionTreeClassifier(random_state=1))])
print('''\n\033[1m''' + '''Checking the achieved test accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved test accuracy of Decision Tree"
Ha = "There is a change in the achieved test accuracy of Decision Tree"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved test accuracies with the different sample population
No change in the achieved test accuracy of Decision Tree as the p_value (nan) > 0.05
print('''\n\033[1m''' + '''Checking the achieved train accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved train accuracy of Decision Tree"
Ha = "There is a change in the achieved train accuracy of Decision Tree"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved train accuracies with the different sample population
No change in the achieved train accuracy of Decision Tree as the p_value (nan) > 0.05
TP,TN,FP,FN = ConfusionMatrix(cm6)
Accuracy=(TP+TN)/(TP+TN+FP+FN)
print('Accuracy of Decision Tree classifier test set: {:.2%}'.format(Accuracy))
acc_arr.append(Accuracy)
Misclassification_Rate=(FP+FN)/(TP+TN+FP+FN)
print('Misclassification Rate: It is often wrong: {:.2%}'.format(Misclassification_Rate))
mis_arr.append(Misclassification_Rate)
#Recall
Sensitivity=TP/(FN+TP)
print('Sensitivity: When its actually yes how often it predicts yes: {:.2%}'.format(Sensitivity))
sen_arr.append(Sensitivity)
Specificity=TN/(TN+FP)
print('Specificity: When its actually no, how often does it predict no: {:.2%}'.format(Specificity))
spec_arr.append(Specificity)
Precision=TP/(FP+TP)
print('Precision: When it predicts yes, how often is it correct: {:.2%}'.format(Precision))
prec_arr.append(Precision)
Accuracy of Decision Tree classifier test set: 100.00% Misclassification Rate: It is often wrong: 0.00% Sensitivity: When its actually yes how often it predicts yes: 100.00% Specificity: When its actually no, how often does it predict no: 100.00% Precision: When it predicts yes, how often is it correct: 100.00%
rfcl = RandomForestClassifier(n_estimators = 50, random_state=1,max_features=12)
rfcl = rfcl.fit(nx_train, ny_train)
y_predict = rfcl.predict(nx_test)
model_train = rfcl.score(nx_train,ny_train)
model_test = rfcl.score(nx_test,ny_test)
score_train['RNDM FRST TRAIN'] = model_train
score_test['RNDM FRST TEST'] = model_test
print('''\n\033[1m''' + '''RNDM FRST TRAIN SCORE''' + '''\033[0m''',model_train)
print('''\n\033[1m''' + '''RNDM FRST TEST SCORE''' + '''\033[0m''',model_test)
print('''\033[1m''' + '''\t\tRANDOM FOREST MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',classification_report(ny_test, y_pred))
print('''\n\n\033[1m''' + '''RANDOM FOREST MODEL CONFUSION METRICS WITH HEATMAP\n''' + '''\033[0m''',confusion_matrix(ny_test, y_pred))
cm7 = metrics.confusion_matrix(ny_test, y_pred)
plt.figure(figsize = (5,3))
sns.heatmap(cm7, annot=True,cmap='Blues', fmt='g')
RNDM FRST TRAIN SCORE 1.0 RNDM FRST TEST SCORE 0.9988610478359908 RANDOM FOREST MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 1.00 1.00 1.00 415 1.0 1.00 1.00 1.00 463 accuracy 1.00 878 macro avg 1.00 1.00 1.00 878 weighted avg 1.00 1.00 1.00 878 RANDOM FOREST MODEL CONFUSION METRICS WITH HEATMAP [[415 0] [ 0 463]]
<AxesSubplot:>
# define models and parameters
model = RandomForestClassifier()
grid = {'n_estimators': [6, 25],
'max_features': [5, 6],
'max_depth': [10, 50, None],
'bootstrap': [True, False]}
# define grid search
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
randomCV = RandomizedSearchCV(model, param_distributions=grid, n_iter=10,cv=cv,
scoring='accuracy',error_score=0, iid=True)
grid_result =randomCV.fit(nx, ny)
# summarize results
print(f"Best model score of Random Forest using {grid_result.best_params_} is {grid_result.best_score_:.3f}")
Best model score of Random Forest using {'n_estimators': 25, 'max_features': 5, 'max_depth': None, 'bootstrap': True} is 1.000
rfcl = RandomForestClassifier(n_estimators= 25, max_features= 6, max_depth = 50, bootstrap = False)
rfcl = rfcl.fit(nx_train, ny_train)
y_predict = rfcl.predict(nx_test)
model_train = rfcl.score(nx_train,ny_train)
model_test = rfcl.score(nx_test,ny_test)
score_train['RNDM FRST TRAIN'] = model_train
score_test['RNDM FRST TEST'] = model_test
print('''\n\033[1m''' + '''RNDM FRST TRAIN SCORE''' + '''\033[0m''',model_train)
print('''\n\033[1m''' + '''RNDM FRST TEST SCORE''' + '''\033[0m''',model_test)
print('''\033[1m''' + '''\t\tRANDOM FOREST MODEL CLASSIFICATION REPORT\n''' + '''\033[0m''',classification_report(ny_test, y_pred))
print('''\n\n\033[1m''' + '''RANDOM FOREST MODEL CONFUSION METRICS WITH HEATMAP\n''' + '''\033[0m''',confusion_matrix(ny_test, y_pred))
cm8 = metrics.confusion_matrix(ny_test, y_pred)
plt.figure(figsize = (5,3))
sns.heatmap(cm8, annot=True,cmap='Blues', fmt='g')
RNDM FRST TRAIN SCORE 1.0 RNDM FRST TEST SCORE 1.0 RANDOM FOREST MODEL CLASSIFICATION REPORT precision recall f1-score support -1.0 1.00 1.00 1.00 415 1.0 1.00 1.00 1.00 463 accuracy 1.00 878 macro avg 1.00 1.00 1.00 878 weighted avg 1.00 1.00 1.00 878 RANDOM FOREST MODEL CONFUSION METRICS WITH HEATMAP [[415 0] [ 0 463]]
<AxesSubplot:>
pipe= make_pipeline(StandardScaler(),( RandomForestClassifier(n_estimators = 50, random_state=1,max_features=12)))
pipe.fit(X_train_d, y_train_d)
pipe.fit(X_test_d, y_test_d)
Pipeline(steps=[('standardscaler', StandardScaler()),
('randomforestclassifier',
RandomForestClassifier(max_features=12, n_estimators=50,
random_state=1))])
print('''\n\033[1m''' + '''Checking the achieved test accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved test accuracy of Random Forest"
Ha = "There is a change in the achieved test accuracy of Random Forest"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved test accuracies with the different sample population
No change in the achieved test accuracy of Random Forest as the p_value (nan) > 0.05
print('''\n\033[1m''' + '''Checking the achieved train accuracies with the different sample population''' + '''\033[0m''')
Ho = "No change in the achieved train accuracy of Random Forest"
Ha = "There is a change in the achieved train accuracy of Random Forest"
x = grid_result.best_score_
y = FindAccuracy(X_train_d,y_train_d,X_test_d,y_test_d)
t, p_value = stats.ttest_ind(x,y, axis = 0)
if p_value < 0.05:
print(f'{Ha} as the p_value ({p_value.round(3)}) < 0.05')
else:
print(f'{Ho} as the p_value ({p_value.round(3)}) > 0.05')
Checking the achieved train accuracies with the different sample population
No change in the achieved train accuracy of Random Forest as the p_value (nan) > 0.05
TP,TN,FP,FN = ConfusionMatrix(cm7)
Accuracy=(TP+TN)/(TP+TN+FP+FN)
print('Accuracy of Random Forest classifier test set: {:.2%}'.format(Accuracy))
acc_arr.append(Accuracy)
Misclassification_Rate=(FP+FN)/(TP+TN+FP+FN)
print('Misclassification Rate: It is often wrong: {:.2%}'.format(Misclassification_Rate))
mis_arr.append(Misclassification_Rate)
#Recall
Sensitivity=TP/(FN+TP)
print('Sensitivity: When its actually yes how often it predicts yes: {:.2%}'.format(Sensitivity))
sen_arr.append(Sensitivity)
Specificity=TN/(TN+FP)
print('Specificity: When its actually no, how often does it predict no: {:.2%}'.format(Specificity))
spec_arr.append(Specificity)
Precision=TP/(FP+TP)
print('Precision: When it predicts yes, how often is it correct: {:.2%}'.format(Precision))
prec_arr.append(Precision)
Accuracy of Random Forest classifier test set: 100.00% Misclassification Rate: It is often wrong: 0.00% Sensitivity: When its actually yes how often it predicts yes: 100.00% Specificity: When its actually no, how often does it predict no: 100.00% Precision: When it predicts yes, how often is it correct: 100.00%
clfs = []
clfs.append(LogisticRegression())
clfs.append(SVC())
clfs.append(KNeighborsClassifier(n_neighbors=optimal_k))
clfs.append(DecisionTreeClassifier())
clfs.append(RandomForestClassifier())
for classifier in clfs:
kfold = KFold(n_splits = 25, random_state = 56)
model_train= cross_val_score(classifier,nx_train,ny_train,cv = kfold)
model_test= cross_val_score(classifier,nx_test,ny_test,cv = kfold)
score_train['CV SCORE OF TRAIN',str(classifier)] = model_train.mean()
score_test['CV SCORE OF TEST',str(classifier)] = model_test.mean()
print('===========================================================')
print('KFOLD TRAIN SCORE(OVERALL)',str(classifier),'{:.2f}% WITH STD+/-({:.2f}%)'.format(model_train.mean(),model_train.std()))
print('KFOLD TEST SCORE(OVERALL)',str(classifier),'{:.2f}% WITH STD+/-({:.2f}%)'.format(model_test.mean(),model_test.std()))
=========================================================== KFOLD TRAIN SCORE(OVERALL) LogisticRegression() 0.83% WITH STD+/-(0.04%) KFOLD TEST SCORE(OVERALL) LogisticRegression() 0.83% WITH STD+/-(0.06%) =========================================================== KFOLD TRAIN SCORE(OVERALL) SVC() 0.98% WITH STD+/-(0.01%) KFOLD TEST SCORE(OVERALL) SVC() 0.94% WITH STD+/-(0.04%) =========================================================== KFOLD TRAIN SCORE(OVERALL) KNeighborsClassifier(n_neighbors=1) 0.96% WITH STD+/-(0.03%) KFOLD TEST SCORE(OVERALL) KNeighborsClassifier(n_neighbors=1) 0.89% WITH STD+/-(0.05%) =========================================================== KFOLD TRAIN SCORE(OVERALL) DecisionTreeClassifier() 0.96% WITH STD+/-(0.02%) KFOLD TEST SCORE(OVERALL) DecisionTreeClassifier() 0.91% WITH STD+/-(0.04%) =========================================================== KFOLD TRAIN SCORE(OVERALL) RandomForestClassifier() 1.00% WITH STD+/-(0.00%) KFOLD TEST SCORE(OVERALL) RandomForestClassifier() 0.98% WITH STD+/-(0.02%)
for classifier in clfs:
print('PCA WITH',str(classifier))
pipe= Pipeline([('pca', PCA()), (str(classifier), classifier)])
pipe.fit(nx_train,ny_train)
pipe.fit(nx_test,ny_test)
model_train = pipe.score(nx_train,ny_train)
model_test = pipe.score(nx_test,ny_test)
score_train['PCA TRAIN',str(classifier)] = model_train
score_test['PCA TEST',str(classifier)] = model_test
print('PCA TRAIN SCORE OF',(str(classifier)," {:.2f}". format( pipe.score( nx_train, ny_train))))
print('PCA TRAIN SCORE OF',(str(classifier)," {:.2f}". format( pipe.score( nx_test, ny_test))))
y_pred = pipe.predict(nx_test)
print((str(classifier)),'PCA CLASSIFICATION REPORT OF\n',classification_report(ny_test, y_pred))
print((str(classifier)),'PCA CONFUSION METRICS WITH HEATMAP OF\n',confusion_matrix(ny_test, y_pred),'\n\n')
cm = metrics.confusion_matrix(ny_test, y_pred)
plt.figure(figsize = (5,3))
sns.heatmap(cm, annot=True,cmap='Blues', fmt='g')
plt.show()
print('==========================================================')
TP,TN,FP,FN = ConfusionMatrix(cm)
Accuracy=(TP+TN)/(TP+TN+FP+FN)
print('Accuracy of',(str(classifier),'test set after PCA: {:.2%}'.format(Accuracy)))
acc_arr.append(Accuracy)
Misclassification_Rate=(FP+FN)/(TP+TN+FP+FN)
print('Misclassification Rate: It is often wrong: {:.2%}'.format(Misclassification_Rate))
mis_arr.append(Misclassification_Rate)
#Recall
Sensitivity=TP/(FN+TP)
print('Sensitivity: When its actually yes how often it predicts yes: {:.2%}'.format(Sensitivity))
sen_arr.append(Sensitivity)
Specificity=TN/(TN+FP)
print('Specificity: When its actually no, how often does it predict no: {:.2%}'.format(Specificity))
spec_arr.append(Specificity)
Precision=TP/(FP+TP)
print('Precision: When it predicts yes, how often is it correct: {:.2%}'.format(Precision))
prec_arr.append(Precision)
PCA WITH LogisticRegression()
PCA TRAIN SCORE OF ('LogisticRegression()', ' 0.82')
PCA TRAIN SCORE OF ('LogisticRegression()', ' 0.91')
LogisticRegression() PCA CLASSIFICATION REPORT OF
precision recall f1-score support
-1.0 0.93 0.87 0.90 415
1.0 0.89 0.94 0.92 463
accuracy 0.91 878
macro avg 0.91 0.91 0.91 878
weighted avg 0.91 0.91 0.91 878
LogisticRegression() PCA CONFUSION METRICS WITH HEATMAP OF
[[361 54]
[ 27 436]]
==========================================================
Accuracy of ('LogisticRegression()', 'test set after PCA: 90.77%')
Misclassification Rate: It is often wrong: 9.23%
Sensitivity: When its actually yes how often it predicts yes: 94.17%
Specificity: When its actually no, how often does it predict no: 86.99%
Precision: When it predicts yes, how often is it correct: 88.98%
PCA WITH SVC()
PCA TRAIN SCORE OF ('SVC()', ' 0.93')
PCA TRAIN SCORE OF ('SVC()', ' 0.99')
SVC() PCA CLASSIFICATION REPORT OF
precision recall f1-score support
-1.0 0.99 0.99 0.99 415
1.0 0.99 0.99 0.99 463
accuracy 0.99 878
macro avg 0.99 0.99 0.99 878
weighted avg 0.99 0.99 0.99 878
SVC() PCA CONFUSION METRICS WITH HEATMAP OF
[[411 4]
[ 5 458]]
==========================================================
Accuracy of ('SVC()', 'test set after PCA: 98.97%')
Misclassification Rate: It is often wrong: 1.03%
Sensitivity: When its actually yes how often it predicts yes: 98.92%
Specificity: When its actually no, how often does it predict no: 99.04%
Precision: When it predicts yes, how often is it correct: 99.13%
PCA WITH KNeighborsClassifier(n_neighbors=1)
PCA TRAIN SCORE OF ('KNeighborsClassifier(n_neighbors=1)', ' 0.88')
PCA TRAIN SCORE OF ('KNeighborsClassifier(n_neighbors=1)', ' 1.00')
KNeighborsClassifier(n_neighbors=1) PCA CLASSIFICATION REPORT OF
precision recall f1-score support
-1.0 1.00 1.00 1.00 415
1.0 1.00 1.00 1.00 463
accuracy 1.00 878
macro avg 1.00 1.00 1.00 878
weighted avg 1.00 1.00 1.00 878
KNeighborsClassifier(n_neighbors=1) PCA CONFUSION METRICS WITH HEATMAP OF
[[415 0]
[ 0 463]]
==========================================================
Accuracy of ('KNeighborsClassifier(n_neighbors=1)', 'test set after PCA: 100.00%')
Misclassification Rate: It is often wrong: 0.00%
Sensitivity: When its actually yes how often it predicts yes: 100.00%
Specificity: When its actually no, how often does it predict no: 100.00%
Precision: When it predicts yes, how often is it correct: 100.00%
PCA WITH DecisionTreeClassifier()
PCA TRAIN SCORE OF ('DecisionTreeClassifier()', ' 0.96')
PCA TRAIN SCORE OF ('DecisionTreeClassifier()', ' 1.00')
DecisionTreeClassifier() PCA CLASSIFICATION REPORT OF
precision recall f1-score support
-1.0 1.00 1.00 1.00 415
1.0 1.00 1.00 1.00 463
accuracy 1.00 878
macro avg 1.00 1.00 1.00 878
weighted avg 1.00 1.00 1.00 878
DecisionTreeClassifier() PCA CONFUSION METRICS WITH HEATMAP OF
[[415 0]
[ 0 463]]
==========================================================
Accuracy of ('DecisionTreeClassifier()', 'test set after PCA: 100.00%')
Misclassification Rate: It is often wrong: 0.00%
Sensitivity: When its actually yes how often it predicts yes: 100.00%
Specificity: When its actually no, how often does it predict no: 100.00%
Precision: When it predicts yes, how often is it correct: 100.00%
PCA WITH RandomForestClassifier()
PCA TRAIN SCORE OF ('RandomForestClassifier()', ' 1.00')
PCA TRAIN SCORE OF ('RandomForestClassifier()', ' 1.00')
RandomForestClassifier() PCA CLASSIFICATION REPORT OF
precision recall f1-score support
-1.0 1.00 1.00 1.00 415
1.0 1.00 1.00 1.00 463
accuracy 1.00 878
macro avg 1.00 1.00 1.00 878
weighted avg 1.00 1.00 1.00 878
RandomForestClassifier() PCA CONFUSION METRICS WITH HEATMAP OF
[[415 0]
[ 0 463]]
==========================================================
Accuracy of ('RandomForestClassifier()', 'test set after PCA: 100.00%')
Misclassification Rate: It is often wrong: 0.00%
Sensitivity: When its actually yes how often it predicts yes: 100.00%
Specificity: When its actually no, how often does it predict no: 100.00%
Precision: When it predicts yes, how often is it correct: 100.00%
import numpy as np
import matplotlib.pyplot as plt
w=.4
X = np.arange(len(score_train))
ig, ax = plt.subplots(figsize=(40,19))
ax.bar(X, score_train.values(), width=w, color='b', align='center')
ax.bar(X+w+.001, score_test.values(), width=w, color='g', align='center')
ax.legend(('Train Score','Test Score'),fontsize = 40)
ax.set_xlabel('Classifiers', fontsize = 40)
ax.set_ylabel('Train & Test Accuracy Measures', fontsize = 35)
ax.set_title('Comparison of all the Classifier Models', fontsize = 40)
ax.tick_params(labelsize=30)
#Changing alignment of labels in x axis
for c in ax.get_xticklabels():
c.set_rotation(80)
plt.xticks(X, score_train.keys())
plt.title("Accuracy score", fontsize=40)
plt.show()
scaink=score_train.keys()
scestk=score_test.keys()
scainv=score_train.values()
scestv=score_test.values()
print('''\n\033[1m''' + '''Displaying and comparing all the models designed with their train and test accuracies''' + '''\033[0m''')
results = pd.DataFrame({'Train Model' : scaink,
'Train Accuracy': scainv,
'Test Model' : scestk,'Test Accuracy' : scestv})
results
Displaying and comparing all the models designed with their train and test accuracies
| Train Model | Train Accuracy | Test Model | Test Accuracy | |
|---|---|---|---|---|
| 0 | LOG-REG TRAIN | 0.819336 | LOG-REG TEST | 0.907745 |
| 1 | LOG-REG KFLOD TRAIN SCORE | 0.829130 | LOG-REG KFLOD TEST SCORE | 0.834889 |
| 2 | LOG-REG LOOCV TRAIN SCORE | 0.831543 | LOG-REG LOOCV TEST SCORE | 0.838269 |
| 3 | LOG-REG TUINING TRAIN | 0.817383 | LOG-REG TUINING TEST | 0.908884 |
| 4 | LOG-REG PCA TRAIN | 0.819336 | LOG-REG PCA TEST | 0.907745 |
| 5 | kNN TRAIN | 1.000000 | kNN TEST | 0.973804 |
| 6 | SVM TRAIN | 1.000000 | SVM TEST | 1.000000 |
| 7 | DTREEE TRAIN | 0.909180 | DTREE TEST | 1.000000 |
| 8 | RNDM FRST TRAIN | 1.000000 | RNDM FRST TEST | 1.000000 |
| 9 | (CV SCORE OF TRAIN, LogisticRegression()) | 0.829130 | (CV SCORE OF TEST, LogisticRegression()) | 0.834889 |
| 10 | (CV SCORE OF TRAIN, SVC()) | 0.982909 | (CV SCORE OF TEST, SVC()) | 0.939810 |
| 11 | (CV SCORE OF TRAIN, KNeighborsClassifier(n_nei... | 0.955598 | (CV SCORE OF TEST, KNeighborsClassifier(n_neig... | 0.891810 |
| 12 | (CV SCORE OF TRAIN, DecisionTreeClassifier()) | 0.958007 | (CV SCORE OF TEST, DecisionTreeClassifier()) | 0.911270 |
| 13 | (CV SCORE OF TRAIN, RandomForestClassifier()) | 0.999024 | (CV SCORE OF TEST, RandomForestClassifier()) | 0.980698 |
| 14 | (PCA TRAIN, LogisticRegression()) | 0.819336 | (PCA TEST, LogisticRegression()) | 0.907745 |
| 15 | (PCA TRAIN, SVC()) | 0.928711 | (PCA TEST, SVC()) | 0.989749 |
| 16 | (PCA TRAIN, KNeighborsClassifier(n_neighbors=1)) | 0.879395 | (PCA TEST, KNeighborsClassifier(n_neighbors=1)) | 1.000000 |
| 17 | (PCA TRAIN, DecisionTreeClassifier()) | 0.955078 | (PCA TEST, DecisionTreeClassifier()) | 1.000000 |
| 18 | (PCA TRAIN, RandomForestClassifier()) | 0.995605 | (PCA TEST, RandomForestClassifier()) | 1.000000 |
import pickle
# save the model to disk
filename = 'best_model.pkl'
pickle.dump(rfcl, open(filename, 'wb'))
fp = pd.read_excel("Future_predictions.xlsx")
fp.head(3)
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| 0 | 2008-07-19 11:55:00 | 3030.93 | 2564.00 | 2187.7333 | 1411.1265 | 1.3602 | 100 | 97.6133 | 0.1242 | 1.5005 | 0.0162 | -0.0034 | 0.9455 | 202.4396 | 0 | 7.9558 | 414.8710 | 10.0433 | 0.9680 | 192.3963 | 12.5190 | 1.4026 | -5419.00 | 2916.50 | -4043.75 | 751.00 | 0.8955 | 1.7730 | 3.0490 | 64.2333 | 2.0222 | 0.1632 | 3.5191 | 83.3971 | 9.5126 | 50.6170 | 64.2588 | 49.3830 | 66.3141 | 86.9555 | 117.5132 | 61.29 | 4.515 | 70 | 352.7173 | 10.1841 | 130.3691 | 723.3092 | 1.3072 | 141.2282 | 1 | 624.3145 | 218.3174 | 0 | 4.592 | 4.841 | 2834 | 0.9317 | 0.9484 | 4.7057 | -1.7264 | 350.9264 | 10.6231 | 108.6427 | 16.1445 | 21.7264 | 29.5367 | 693.7724 | 0.9226 | 148.6009 | 1 | 608.1700 | 84.0793 | NaN | NaN | 0 | 0.0126 | -0.0206 | 0.0141 | -0.0307 | -0.0083 | -0.0026 | -0.0567 | -0.0044 | 7.2163 | 0.1320 | NaN | 2.3895 | 0.9690 | 1747.6049 | 0.1841 | 8671.9301 | -0.3274 | -0.0055 | -0.0001 | 0.0001 | 0.0003 | -0.2786 | 0 | 0.3974 | -0.0251 | 0.0002 | 0.0002 | 0.1350 | -0.0042 | 0.0003 | 0.0056 | 0.0000 | -0.2468 | 0.3196 | NaN | NaN | NaN | NaN | 0.9460 | 0 | 748.6115 | 0.9908 | 58.4306 | 0.6002 | 0.9804 | 6.3788 | 15.88 | 2.639 | 15.94 | 15.93 | 0.8656 | 3.353 | 0.4098 | 3.188 | -0.0473 | 0.7243 | 0.9960 | 2.2967 | 1000.7263 | 39.2373 | 123 | 111.3 | 75.2 | 46.2000 | 350.6710 | 0.3948 | 0 | 6.78 | 0.0034 | 0.0898 | 0.0850 | 0.0358 | 0.0328 | 12.2566 | 0 | 4.271 | 10.284 | 0.4734 | 0.0167 | 11.8901 | 0.41 | 0.0506 | NaN | NaN | 1017 | 967 | 1066 | 368 | 0.090 | 0.048 | 0.095 | 2.0 | 0.9 | 0.069 | 0.046 | 0.7250 | 0.1139 | 0.3183 | 0.5888 | 0.3184 | 0.9499 | 0.3979 | 0.160 | 0 | 0 | 20.95 | 0.333 | 12.49 | 16.713 | 0.0803 | 5.72 | 0 | 11.19 | 65.363 | 0 | 0 | 0 | 0 | 0 | 0 | 0.292 | 5.38 | 20.10 | 0.296 | 10.62 | 10.30 | 5.38 | 4.040 | 16.230 | 0.2951 | 8.64 | 0 | 10.30 | 97.314 | 0 | 0.0772 | 0.0599 | 0.0700 | 0.0547 | 0.0704 | 0.0520 | 0.0301 | 0.1135 | 3.4789 | 0.0010 | NaN | 0.0707 | 0.0211 | 175.2173 | 0.0315 | 1940.3994 | 0 | 0.0744 | 0.0546 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0027 | 0.0040 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | 0.0188 | 0 | 219.9453 | 0.0011 | 2.8374 | 0.0189 | 0.0050 | 0.4269 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0472 | 40.855 | 4.5152 | 30.9815 | 33.9606 | 22.9057 | 15.9525 | 110.2144 | 0.1310 | 0 | 2.5883 | 0.0010 | 0.0319 | 0.0197 | 0.0120 | 0.0109 | 3.9321 | 0 | 1.5123 | 3.5811 | 0.1337 | 0.0055 | 3.8447 | 0.1077 | 0.0167 | NaN | NaN | 418.1363 | 398.3185 | 496.1582 | 158.3330 | 0.0373 | 0.0202 | 0.0462 | 0.6083 | 0.3032 | 0.0200 | 0.0174 | 0.2827 | 0.0434 | 0.1342 | 0.2419 | 0.1343 | 0.3670 | 0.1431 | 0.0610 | 0 | 0 | 0 | 6.2698 | 0.1181 | 3.8208 | 5.3737 | 0.0254 | 1.6252 | 0 | 3.2461 | 18.0118 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0752 | 1.5989 | 6.5893 | 0.0913 | 3.0911 | 8.4654 | 1.5989 | 1.2293 | 5.3406 | 0.0867 | 2.8551 | 0 | 2.9971 | 31.8843 | NaN | NaN | 0 | 0.0215 | 0.0274 | 0.0315 | 0.0238 | 0.0206 | 0.0238 | 0.0144 | 0.0491 | 1.2708 | 0.0004 | NaN | 0.0229 | 0.0065 | 55.2039 | 0.0105 | 560.2658 | 0 | 0.0170 | 0.0148 | 0.0124 | 0.0114 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0010 | 0.0013 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | 0.0055 | 0 | 61.5932 | 0.0003 | 0.9967 | 0.0082 | 0.0017 | 0.1437 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0151 | 14.2396 | 1.4392 | 5.6188 | 3.6721 | 2.9329 | 2.1118 | 24.8504 | 29.0271 | 0 | 6.9458 | 2.7380 | 5.9846 | 525.0965 | 0.0000 | 3.4641 | 6.0544 | 0 | 53.6840 | 2.4788 | 4.7141 | 1.7275 | 6.1800 | 3.2750 | 3.6084 | 18.7673 | 33.1562 | 26.3617 | 49.0013 | 10.0503 | 2.7073 | 3.1158 | 3.1136 | 44.5055 | 42.2737 | 1.3071 | 0.8693 | 1.1975 | 0.6288 | 0.9163 | 0.6448 | 1.4324 | 0.4576 | 0.1362 | 0 | 0 | 0 | 5.9396 | 3.2698 | 9.5805 | 2.3106 | 6.1463 | 4.0502 | 0 | 1.7924 | 29.9394 | 0 | 0 | 0 | 0 | 0 | 0 | 6.2052 | 311.6377 | 5.7277 | 2.7864 | 9.7752 | 63.7987 | 24.7625 | 13.6778 | 2.3394 | 31.9893 | 5.8142 | 0 | 1.6936 | 115.7408 | 0 | 613.3069 | 291.4842 | 494.6996 | 178.1759 | 843.1138 | 0.0000 | 53.1098 | 0.0000 | 48.2091 | 0.7578 | NaN | 2.9570 | 2.1739 | 10.0261 | 17.1202 | 22.3756 | 0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 64.6707 | 0.0000 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | 1.9864 | 0 | 29.3804 | 0.1094 | 4.8560 | 3.1406 | 0.5064 | 6.6926 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2.0570 | 4.0825 | 11.5074 | 0.1096 | 0.0078 | 0.0026 | 7.116 | 1.0616 | 395.570 | 75.752 | 0.4234 | 12.93 | 0.78 | 0.1827 | 5.7349 | 0.3363 | 39.8842 | 3.2687 | 1.0297 | 1.0344 | 0.4385 | 0.1039 | 42.3877 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 533.8500 | 2.1113 | 8.95 | 0.3157 | 3.0624 | 0.1026 | 1.6765 | 14.9509 | NaN | NaN | NaN | NaN | 0.5005 | 0.0118 | 0.0035 | 2.3630 | NaN | NaN | NaN | NaN |
| 1 | 2008-07-19 12:32:00 | 3095.78 | 2465.14 | 2230.4222 | 1463.6606 | 0.8294 | 100 | 102.3433 | 0.1247 | 1.4966 | -0.0005 | -0.0148 | 0.9627 | 200.5470 | 0 | 10.1548 | 414.7347 | 9.2599 | 0.9701 | 191.2872 | 12.4608 | 1.3825 | -5441.50 | 2604.25 | -3498.75 | -1640.25 | 1.2973 | 2.0143 | 7.3900 | 68.4222 | 2.2667 | 0.2102 | 3.4171 | 84.9052 | 9.7997 | 50.6596 | 64.2828 | 49.3404 | 64.9193 | 87.5241 | 118.1188 | 78.25 | 2.773 | 70 | 352.2445 | 10.0373 | 133.1727 | 724.8264 | 1.2887 | 145.8445 | 1 | 631.2618 | 205.1695 | 0 | 4.590 | 4.842 | 2853 | 0.9324 | 0.9479 | 4.6820 | 0.8073 | 352.0073 | 10.3092 | 113.9800 | 10.9036 | 19.1927 | 27.6301 | 697.1964 | 1.1598 | 154.3709 | 1 | 620.3582 | 82.3494 | NaN | NaN | 0 | -0.0039 | -0.0198 | 0.0004 | -0.0440 | -0.0358 | -0.0120 | -0.0377 | 0.0017 | 6.8043 | 0.1358 | NaN | 2.3754 | 0.9894 | 1931.6464 | 0.1874 | 8407.0299 | 0.1455 | -0.0015 | 0.0000 | -0.0005 | 0.0001 | 0.5854 | 0 | -0.9353 | -0.0158 | -0.0004 | -0.0004 | -0.0752 | -0.0045 | 0.0002 | 0.0015 | 0.0000 | 0.0772 | -0.0903 | NaN | NaN | NaN | NaN | 0.9425 | 0 | 731.2517 | 0.9902 | 58.6680 | 0.5958 | 0.9731 | 6.5061 | 15.88 | 2.541 | 15.91 | 15.88 | 0.8703 | 2.771 | 0.4138 | 3.272 | -0.0946 | 0.8122 | 0.9985 | 2.2932 | 998.1081 | 37.9213 | 98 | 80.3 | 81.0 | 56.2000 | 219.7679 | 0.2301 | 0 | 5.70 | 0.0049 | 0.1356 | 0.0600 | 0.0547 | 0.0204 | 12.3319 | 0 | 6.285 | 13.077 | 0.5666 | 0.0144 | 11.8428 | 0.35 | 0.0437 | NaN | NaN | 568 | 59 | 297 | 3277 | 0.112 | 0.115 | 0.124 | 2.2 | 1.1 | 0.079 | 0.561 | 1.0498 | 0.1917 | 0.4115 | 0.6582 | 0.4115 | 1.0181 | 0.2315 | 0.325 | 0 | 0 | 17.99 | 0.439 | 10.14 | 16.358 | 0.0892 | 6.92 | 0 | 9.05 | 82.986 | 0 | 0 | 0 | 0 | 0 | 0 | 0.222 | 3.74 | 19.59 | 0.316 | 11.65 | 8.02 | 3.74 | 3.659 | 15.078 | 0.3580 | 8.96 | 0 | 8.02 | 134.250 | 0 | 0.0566 | 0.0488 | 0.1651 | 0.1578 | 0.0468 | 0.0987 | 0.0734 | 0.0747 | 3.9578 | 0.0050 | NaN | 0.0761 | 0.0014 | 128.4285 | 0.0238 | 1988.0000 | 0 | 0.0203 | 0.0236 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0064 | 0.0036 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | 0.0154 | 0 | 193.0287 | 0.0007 | 3.8999 | 0.0187 | 0.0086 | 0.5749 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0411 | 29.743 | 3.6327 | 29.0598 | 28.9862 | 22.3163 | 17.4008 | 83.5542 | 0.0767 | 0 | 1.8459 | 0.0012 | 0.0440 | 0.0171 | 0.0154 | 0.0069 | 3.9011 | 0 | 2.1016 | 3.9483 | 0.1662 | 0.0049 | 3.7836 | 0.1000 | 0.0139 | NaN | NaN | 233.9865 | 26.5879 | 139.2082 | 1529.7622 | 0.0502 | 0.0561 | 0.0591 | 0.8151 | 0.3464 | 0.0291 | 0.1822 | 0.3814 | 0.0715 | 0.1667 | 0.2630 | 0.1667 | 0.3752 | 0.0856 | 0.1214 | 0 | 0 | 0 | 5.6522 | 0.1417 | 2.9939 | 5.2445 | 0.0264 | 1.8045 | 0 | 2.7661 | 23.6230 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0778 | 1.1506 | 5.9247 | 0.0878 | 3.3604 | 7.7421 | 1.1506 | 1.1265 | 5.0108 | 0.1013 | 2.4278 | 0 | 2.4890 | 41.7080 | NaN | NaN | 0 | 0.0142 | 0.0230 | 0.0768 | 0.0729 | 0.0143 | 0.0513 | 0.0399 | 0.0365 | 1.2474 | 0.0017 | NaN | 0.0248 | 0.0005 | 46.3453 | 0.0069 | 677.1873 | 0 | 0.0053 | 0.0059 | 0.0081 | 0.0033 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0022 | 0.0013 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | 0.0049 | 0 | 65.0999 | 0.0002 | 1.1655 | 0.0068 | 0.0027 | 0.1921 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0120 | 10.5837 | 1.0323 | 4.3465 | 2.5939 | 3.2858 | 2.5197 | 15.0150 | 27.7464 | 0 | 5.5695 | 3.9300 | 9.0604 | 0.0000 | 368.9713 | 2.1196 | 6.1491 | 0 | 61.8918 | 3.1531 | 6.1188 | 1.4857 | 6.1911 | 2.8088 | 3.1595 | 10.4383 | 2.2655 | 8.4887 | 199.7866 | 8.6336 | 5.7093 | 1.6779 | 3.2153 | 48.5294 | 37.5793 | 16.4174 | 1.2364 | 1.9562 | 0.8123 | 1.0239 | 0.8340 | 1.5683 | 0.2645 | 0.2751 | 0 | 0 | 0 | 5.1072 | 4.3737 | 7.6142 | 2.2568 | 6.9233 | 4.7448 | 0 | 1.4336 | 40.4475 | 0 | 0 | 0 | 0 | 0 | 0 | 4.7415 | 463.2883 | 5.5652 | 3.0652 | 10.2211 | 73.5536 | 19.4865 | 13.2430 | 2.1627 | 30.8643 | 5.8042 | 0 | 1.2928 | 163.0249 | 0 | 0.0000 | 246.7762 | 0.0000 | 359.0444 | 130.6350 | 820.7900 | 194.4371 | 0.0000 | 58.1666 | 3.6822 | NaN | 3.2029 | 0.1441 | 6.6487 | 12.6788 | 23.6469 | 0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 141.4365 | 0.0000 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | 1.6292 | 0 | 26.3970 | 0.0673 | 6.6475 | 3.1310 | 0.8832 | 8.8370 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.7910 | 2.9799 | 9.5796 | 0.1096 | 0.0078 | 0.0026 | 7.116 | 1.3526 | 408.798 | 74.640 | 0.7193 | 16.00 | 1.33 | 0.2829 | 7.1196 | 0.4989 | 53.1836 | 3.9139 | 1.7819 | 0.9634 | 0.1745 | 0.0375 | 18.1087 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 535.0164 | 2.4335 | 5.92 | 0.2653 | 2.0111 | 0.0772 | 1.1065 | 10.9003 | 0.0096 | 0.0201 | 0.0060 | 208.2045 | 0.5019 | 0.0223 | 0.0055 | 4.4447 | 0.0096 | 0.0201 | 0.0060 | 208.2045 |
| 2 | 2008-07-19 13:17:00 | 2932.61 | 2559.94 | 2186.4111 | 1698.0172 | 1.5102 | 100 | 95.4878 | 0.1241 | 1.4436 | 0.0041 | 0.0013 | 0.9615 | 202.0179 | 0 | 9.5157 | 416.7075 | 9.3144 | 0.9674 | 192.7035 | 12.5404 | 1.4123 | -5447.75 | 2701.75 | -4047.00 | -1916.50 | 1.3122 | 2.0295 | 7.5788 | 67.1333 | 2.3333 | 0.1734 | 3.5986 | 84.7569 | 8.6590 | 50.1530 | 64.1114 | 49.8470 | 65.8389 | 84.7327 | 118.6128 | 14.37 | 5.434 | 70 | 364.3782 | 9.8783 | 131.8027 | 734.7924 | 1.2992 | 141.0845 | 1 | 637.2655 | 185.7574 | 0 | 4.486 | 4.748 | 2936 | 0.9139 | 0.9447 | 4.5873 | 23.8245 | 364.5364 | 10.1685 | 115.6273 | 11.3019 | 16.1755 | 24.2829 | 710.5095 | 0.8694 | 145.8000 | 1 | 625.9636 | 84.7681 | 140.6972 | 485.2665 | 0 | -0.0078 | -0.0326 | -0.0052 | 0.0213 | -0.0054 | -0.1134 | -0.0182 | 0.0287 | 7.1041 | 0.1362 | NaN | 2.4532 | 0.9880 | 1685.8514 | 0.1497 | 9317.1698 | 0.0553 | 0.0006 | -0.0013 | 0.0000 | 0.0002 | -0.1343 | 0 | -0.1427 | 0.1218 | 0.0006 | -0.0001 | 0.0134 | -0.0026 | -0.0016 | -0.0006 | 0.0013 | -0.0301 | -0.0728 | NaN | NaN | NaN | 0.4684 | 0.9231 | 0 | 718.5777 | 0.9899 | 58.4808 | 0.6015 | 0.9772 | 6.4527 | 15.90 | 2.882 | 15.94 | 15.95 | 0.8798 | 3.094 | 0.4777 | 3.272 | -0.1892 | 0.8194 | 0.9978 | 2.2592 | 998.4440 | 42.0579 | 89 | 126.4 | 96.5 | 45.1001 | 306.0380 | 0.3263 | 0 | 8.33 | 0.0038 | 0.0754 | 0.0483 | 0.0619 | 0.0221 | 8.2660 | 0 | 4.819 | 8.443 | 0.4909 | 0.0177 | 8.2054 | 0.47 | 0.0497 | NaN | NaN | 562 | 788 | 759 | 2100 | 0.187 | 0.117 | 0.068 | 2.1 | 1.4 | 0.123 | 0.319 | 1.0824 | 0.0369 | 0.3141 | 0.5753 | 0.3141 | 0.9677 | 0.2706 | 0.326 | 0 | 0 | 17.78 | 0.745 | 13.31 | 22.912 | 0.1959 | 9.21 | 0 | 17.87 | 60.110 | 0 | 0 | 0 | 0 | 0 | 0 | 0.139 | 5.09 | 19.75 | 0.949 | 9.71 | 16.73 | 5.09 | 11.059 | 22.624 | 0.1164 | 13.30 | 0 | 16.73 | 79.618 | 0 | 0.0339 | 0.0494 | 0.0696 | 0.0406 | 0.0401 | 0.0840 | 0.0349 | 0.0718 | 2.4266 | 0.0014 | NaN | 0.0963 | 0.0152 | 182.4956 | 0.0284 | 839.6006 | 0 | 0.0192 | 0.0170 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0062 | 0.0040 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0.1729 | 0.0273 | 0 | 104.4042 | 0.0007 | 4.1446 | 0.0733 | 0.0063 | 0.4166 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0487 | 29.621 | 3.9133 | 23.5510 | 41.3837 | 32.6256 | 15.7716 | 97.3868 | 0.1117 | 0 | 2.5274 | 0.0012 | 0.0249 | 0.0152 | 0.0157 | 0.0075 | 2.8705 | 0 | 1.5306 | 2.5493 | 0.1479 | 0.0059 | 2.8046 | 0.1185 | 0.0167 | NaN | NaN | 251.4536 | 329.6406 | 325.0672 | 902.4576 | 0.0800 | 0.0583 | 0.0326 | 0.6964 | 0.4031 | 0.0416 | 0.1041 | 0.3846 | 0.0151 | 0.1288 | 0.2268 | 0.1288 | 0.3677 | 0.1175 | 0.1261 | 0 | 0 | 0 | 5.7247 | 0.2682 | 3.8541 | 6.1797 | 0.0546 | 2.5680 | 0 | 4.6067 | 16.0104 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0243 | 1.5481 | 5.9453 | 0.2777 | 3.1600 | 8.9855 | 1.5481 | 2.9844 | 6.2277 | 0.0353 | 3.7663 | 0 | 5.6983 | 24.7959 | 13.5664 | 15.4488 | 0 | 0.0105 | 0.0208 | 0.0327 | 0.0171 | 0.0116 | 0.0428 | 0.0154 | 0.0383 | 0.7786 | 0.0005 | NaN | 0.0302 | 0.0046 | 58.0575 | 0.0092 | 283.6616 | 0 | 0.0054 | 0.0043 | 0.0030 | 0.0037 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0021 | 0.0015 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0.0221 | 0.0100 | 0 | 28.7334 | 0.0003 | 1.2356 | 0.0190 | 0.0020 | 0.1375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0190 | 11.4871 | 1.1798 | 4.0782 | 4.3102 | 3.7696 | 2.0627 | 18.0233 | 21.6062 | 0 | 8.7236 | 3.0609 | 5.2231 | 0.0000 | 0.0000 | 2.2943 | 4.0917 | 0 | 50.6425 | 2.0261 | 5.2707 | 1.8268 | 4.2581 | 3.7479 | 3.5220 | 10.3162 | 29.1663 | 18.7546 | 109.5747 | 14.2503 | 5.7650 | 0.8972 | 3.1281 | 60.0000 | 70.9161 | 8.8647 | 1.2771 | 0.4264 | 0.6263 | 0.8973 | 0.6301 | 1.4698 | 0.3194 | 0.2748 | 0 | 0 | 0 | 4.8795 | 7.5418 | 10.0984 | 3.1182 | 15.0790 | 6.5280 | 0 | 2.8042 | 32.3594 | 0 | 0 | 0 | 0 | 0 | 0 | 3.0301 | 21.3645 | 5.4178 | 9.3327 | 8.3977 | 148.0287 | 31.4674 | 45.5423 | 3.1842 | 13.3923 | 9.1221 | 0 | 2.6727 | 93.9245 | 0 | 434.2674 | 151.7665 | 0.0000 | 190.3869 | 746.9150 | 74.0741 | 191.7582 | 250.1742 | 34.1573 | 1.0281 | NaN | 3.9238 | 1.5357 | 10.8251 | 18.9849 | 9.0113 | 0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 240.7767 | 244.2748 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 36.9067 | 2.9626 | 0 | 14.5293 | 0.0751 | 7.0870 | 12.1831 | 0.6451 | 6.4568 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2.1538 | 2.9667 | 9.3046 | 0.1096 | 0.0078 | 0.0026 | 7.116 | 0.7942 | 411.136 | 74.654 | 0.1832 | 16.16 | 0.85 | 0.0857 | 7.1619 | 0.3752 | 23.0713 | 3.9306 | 1.1386 | 1.5021 | 0.3718 | 0.1233 | 24.7524 | 267.064 | 0.9032 | 1.1 | 0.6219 | 0.4122 | 0.2562 | 0.4119 | 68.8489 | 535.0245 | 2.0293 | 11.21 | 0.1882 | 4.0923 | 0.0640 | 2.0952 | 9.2721 | 0.0584 | 0.0484 | 0.0148 | 82.8602 | 0.4958 | 0.0157 | 0.0039 | 3.1745 | 0.0584 | 0.0484 | 0.0148 | 82.8602 |
fp.to_csv ("Test.csv",
index = None,
header=True)
# read csv file and convert
# into a dataframe object
fpc = pd.DataFrame(pd.read_csv("Test.csv"))
fpc.shape
(18, 591)
for x in ulf:
fpc=fpc.drop(x,axis=1)
fpc.fillna(0,inplace=True)
fpc.shape
(18, 139)
scaler = StandardScaler().fit(fpc)
X_unseen = scaler.transform(fpc)
loaded_model = pickle.load(open(filename, 'rb'))
Ypredict = loaded_model.predict(X_unseen)
Ypredict
array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,
-1., -1., -1., -1., -1.])
All the predictions indicate Pass label for all the rows in the future data. There can be a possibility of misclassification in the predictions as the misclassification rate during training was 0.23%. And also, the precision was 99.53%.